WO2021250267A1

WO2021250267A1 - A method for early detection of propensity to severe clinical manifestations

Info

Publication number: WO2021250267A1
Application number: PCT/EP2021/065864
Authority: WO
Inventors: Carl SARAH; Knuckles PHILIP; Serva ANDRIUS; Tiwari VIJAY; Nestorov PETER; Tarte KARIN; Roussel MIKAEL; Tadie JEAN-MARC
Original assignee: Scailyte Ag; Chu De Rennes; Université de Rennes I
Priority date: 2020-06-11
Filing date: 2021-06-11
Publication date: 2021-12-16

Abstract

The invention relates to a panel of molecular markers, which in combination of at least two markers define a unique state of the immune system in patients infected with a pathogen, patients suffering from pancreatitis, patients suffering from acute lung injury and/or patients suffering from head, chest and/or other trauma,, wherein the markers are predictive of severe respiratory conditions such as acute respiratory distress syndrome (ARDS), admittance into ICU, development of cytokine release syndrome (CRS) and/or secondary hemophagocytic lymphohistiocytosis (sHLH). The biomarkers provided herein are also provided for monitoring severe respiratory conditions, relapse of the conditions and/or as targets for therapeutic intervention.

Description

A method for early detection of propensity to severe clinical manifestations

A new strain of coronavirus was detected and believed to have emerged in the Wuhan province, China in late 2019. By March 2020, the WHO recognized the virus spread and declared a global pandemic with widespread economic and health impacts. Along with SARS-CoV and Middle East respiratory syndrome-coronavirus (MERS-CoV), SARS-CoV-2 is the third corona-virus to cause severe respiratory illness in humans, called coronavirus disease 2019 (Covid-19); see Moore, J. B. & June, C. H. Cytokine release syndrome in severe COVID-19. Science 368, 473- 474 (2020).

Initial descriptions of the first Covid-19 cases at the end of 2019 showed marked differences to molecularly related corona viruses MERS-CoV and SARS-CoV. The majority of patients experience mild-to-moderate disease, however early data from mainland China of patients confirmed to be infected with Covid-19, indicated the admission to ICU and mechanical ventilation or death occurred in approximately 6% of cases. More recent estimates reach 10- 20% in badly affected regions with vulnerable demographics. This proportion of critical cases easily surpasses the estimates for seasonal Influenza.

Severe disease associated with Covid-19 infection manifests as pneumonia and intense fever leading to acute respiratory distress syndrome (ARDS). The high mortality associated with Covid-19 might be due to virally driven hyperinflammation. Additionally, relatively high rates of respiratory failure are reported in young adults (aged 50 years and below) with previously mild comorbidities such as hypertension, diabetes mellitus and overweight. In severe cases, clinical observations typically describe a two-step disease progression, starting with a mild-to- moderate presentation followed by a secondary respiratory worsening between 9 to 12 days after onset of first symptoms. High concentration of cytokines (termed cytokine storm) has been recorded in the plasma of critically ill patients infected with Covid-19. This is reminiscent of cytokine release syndrome (CRS)-induced ARDS and secondary hemophagocytic lymphohistiocytosis (sHLH) observed in patients with SARS-CoV and MERS-CoV as well as in leukemia patients receiving CAR-T cell therapy. A hallmark of cytokine release is elevated serum interleukin-6 (IL-6) which correlates with respiratory failure, ARDS and adverse outcomes.

The widespread infection of Covid-19 has placed an unprecedented strain on the healthcare infrastructures of all affected countries. As many as 20-30% of Covid-19 patients require admission into ICUs and eventually assisted mechanical ventilation. A predictive tool for the early detection of propensity to severe clinical manifestations, including ARDS development and ICU admittance, in Covid-19 positive patients is currently an urgent unmet need.

However, severe respiratory conditions and in particular the underlying mechanisms leading to such conditions can also be triggered by other pathogen infections, including infections with bacteria or viruses.

In view of the foregoing, there is an urgent need for predictors of severe clinical conditions.

Thus, the technical problem underlying the present invention is the provision of biomarkers predictive of severe clinical conditions in patients infected with a viral and/or bacterial pathogen.

The above technical problem is solved by the embodiments provided herein and as characterized in the claims.

Accordingly, the invention relates to, inter alia, the following embodiments:

1. A panel of markers, which in combination of at least two molecular markers, define a unique state of the immune system in patients infected with a pathogen, patients suffering from pancreatitis, patients suffering from acute lung injury and/or patients suffering from head, chest and/or other trauma, wherein the markers are predictive of a severe respiratory condition. The panel of markers of embodiment 1, comprising at least 3, 4, 5, 6, 7, 8, 9 or 10 molecular markers. The panel of markers of embodiment 1 or 2, wherein the at least two molecular markers are selected from the group consisting of CD163, CD274 (PD-L1), CD326 (EpCAM), CD45RA, CD21, S100A9, CD206, CD209, CD 192 (CCR2), CD254, CD32, CD64, CD 172a, CD123, CD169, CD86, HLA-DR, CD54, CD33, CD36, CD49a, CD31, gp38, CD80, CD34, CD la, CX3R1, CD195, CD68 and CD106. The panel of markers of embodiment 3 further comprising one or more molecular markers selected from the group consisting of PCT, CRP, G-CSF, GM-CSF, CXCL1, IFNg, IL-lra, IL-lb, CXCL8, IL-10, IL12p70, IL-18, IL-6, CXCL10, CCL2, CCL22, CXCL9, TNFa and VEGFa. The panel of markers according to embodiment 3 or 4, wherein the panel additionally comprises one or more cell lineage molecular marker(s), in particular one or more marker(s) selected from the group consisting of CD45, CD3, CD14, CD16, CD19, CD 11c, CD lib, and CD20. The panel of markers according to any one of embodiments 1 to 5 additionally comprising at least one non-molecular marker. The panel of markers according to embodiment 6, wherein the non-molecular marker is at least one marker selected from the group of age, BMI, body temperature, systolic blood pressure, diastolic blood pressure, mean arterial blood pressure, heart rate and respiratory rate. A method for defining a unique state of an immune system, the method comprising the steps of: a) determining the level(s) of expression and/or level(s) of protein of the molecular markers of the panel of markers according to embodiment 1 to 5 in at least one cell, plasma and/or serum; and b) defining a unique state of the immune system based on the levels of expression and/or level(s) of protein of step (a). The method according to embodiments 8, wherein the levels of expression of the molecular markers are determined using an antibody-based assay, in particular wherein the antibody-based assay is an antibody-based flow cytometry or mass cytometry assay. The method according to embodiment 8 or 9, wherein step (a) additionally comprises retrieving the non-molecular markers of the panel of markers according to embodiment 6 or 7 and wherein defining a unique state of the immune system in step (b) is additionally based on the retrieved non-molecular markers of step (a). A method for quantifying the frequency of disease-associated immune cells in a plurality of human cells, the method comprising the steps of: i) defining a unique state of an immune system in a plurality of human cells according to the method according to embodiment 8 to 10; and ii) quantifying the frequency of disease-associated immune cells in the plurality of human cells based on the unique state of the immune system. The method according to embodiment 11, wherein the human cells are immune cells from the myeloid lineage. The method according to embodiment 12, wherein

(I) an increased expression level of at least one marker selected from the group of CD163, CD274 (PD-L1), CD21, CD33, CD32, CD36 and CD192 (CCR2) is indicative of an increased propensity of a severe respiratory condition; and/or

(II) a reduced expression level of at least one marker selected from the group of CD49a, CD 172a, CDllc, CD123, CD169, CD86, HLA-DR, CD123 / IL-3R and CD45RA is indicative of an increased propensity of a severe respiratory condition, preferably wherein for (I) and (II) increased or reduced expression level, respectively, is with respect to expression levels in cells in absence of a severe respiratory condition, preferably wherein for (I) and (II) increased propensity of severe respiratory conditions is with respect to the average propensity of a severe respiratory condition. The method according to any one of embodiments 11 to 13, wherein a classifier algorithm is used for quantifying the frequency of disease-associated immune cells in the plurality of human cells based on the unique state of the immune system. The method according to embodiment 14, wherein the classifier algorithm comprises a vector algorithm, a convolutional neural network, a tree-based method, logistic regression The method according to embodiments 14 or 15, wherein the classifier algorithm is used to distinguish between immune cell populations indicative of severe respiratory conditions and immune cell populations contraindicative of severe respiratory conditions. The method according to any one of embodiments 14 to 16, wherein the classifier algorithm is a selection algorithm. The method according to any one of embodiments 14 to 17, wherein the classifier algorithm is a computer-implemented algorithm. A computer program product containing instructions for performing the computer- implemented method according to embodiment 18. A method for predicting a clinical outcome in a patient infected with a pathogen, patients suffering from pancreatitis, a patient suffering from acute inhalation injury and/or a patient suffering from head, chest and/or other trauma, the method comprising the steps of:

(a) defining a unique state of the immune system according to the method of any one of embodiments 8 to 10 and/or quantifying the frequency of disease-associated immune cells according to the method of any one of embodiments 11 to 18;

(b)comparing the unique state of the immune system and/or the frequency of disease- associated immune cells of step (a) to a predictability reference pattern, wherein the predictability reference pattern is obtained from a reference population with a known clinical outcome; and c) determining the clinical outcome of the patient based on the comparison in step (b). A method for determining susceptibility of a patient towards a respiratory treatment composition comprising the steps of: a) defining a unique state of the immune system according to the method of any one of embodiments 8 to 10 and/or quantifying the frequency of disease-associated immune cells according to the method of any one of embodiments 11 to 18; b) comparing the unique state of the immune system and/or the frequency of disease- associated immune cells of step (a) to a susceptibility reference pattern, wherein the susceptibility reference pattern is obtained from a reference population with known treatment outcome; and c) determining susceptibility of a patient towards a respiratory treatment composition based on the comparison of step (b). The method for predicting a clinical outcome according to embodiment 20 or method for determining susceptibility according to embodiment 21, wherein determining susceptibility of a patient towards a respiratory treatment composition or determining the clinical outcome of the patient comprises the use of a classifier algorithm, in particular wherein the classifier algorithm comprises a convoluted neural network and/or logistic regression. The method for predicting a clinical outcome according to embodiment 20 or 22 or method for determining susceptibility according to embodiment 21 or 22, wherein the reference population comprises at least one reference subject not having a severe respiratory condition. The method for predicting a clinical outcome according to any one of the embodiments 20, 22 or 23 or method for determining susceptibility according to any one of the embodiments 21 to 23, wherein the reference population additionally comprises at least one reference subject that is a patient having a severe respiratory condition. A composition for use in the treatment of a severe respiratory condition in a) a patient infected with at least one pathogen, b) a patient suffering from acute lung injury, c) patients suffering from pancreatitis, and/or d) a patient suffering from head, chest and/or other trauma, wherein the patient is determined as susceptible towards a treatment according to the method of any one of the embodiments 21 to 24. The panel of markers according to any one of embodiments 1 to 7, the method for defining a unique state of an immune system according to any one of embodiments 8 to 10, the method for quantifying the frequency of disease-associated immune cells according to any one of embodiments 11 to 18, the computer program product according to embodiment 19, the method for predicting a clinical outcome according to any one of the embodiments 20, 22 to 24, the composition for use according to embodiment 25 or method for determining susceptibility according to any one of the embodiments 21 to 24, wherein the severe respiratory condition comprises at least one condition selected from the group of acute respiratory distress syndrome (ARDS) status, admittance into ICU, requirement of ventilatory management, development of cytokine release syndrome (CRS) and secondary hemophagocytic lymphohistiocytosis (sHLH).

27. The panel of markers according to embodiment 26, the method for defining a unique state of an immune system according to embodiment 26, the method for quantifying the frequency of disease-associated immune cells according to embodiment 26, the computer program product according to embodiment 26, the method for predicting a clinical outcome according to embodiment 26, the composition for use according to embodiment 26 or method for determining susceptibility according to embodiment 26, wherein the severe respiratory condition is ARDS, admittance into ICU and/or requirement of ventilatory management.

28. The panel of markers according to any one of embodiments 1 to 7, 26 or 27, the method for defining a unique state of an immune system according to any one of embodiments 8 to 10, 26 or 27, the method for quantifying the frequency of disease-associated immune cells according to any one of embodiments 11 to 18, 26 or 27, the computer program product according to embodiment 19, 26 or 27, the method for predicting a clinical outcome according to any one of the embodiments 20, 22 to 26, the composition for use according to any one of embodiments 25 to 27 or method for determining susceptibility according to any one of the embodiments 21 to 24, 26, 27, wherein the pathogen is SAR.S- CoV-2.

Accordingly, in a first embodiment, the invention relates to a panel of markers, which in combination of at least two molecular markers, define a unique state of the immune system in patients infected with a pathogen, patients suffering from pancreatitis, patients suffering from acute lung injury and/or patients suffering from head, chest and/or other trauma, wherein the markers are predictive of a severe respiratory condition.

The term “molecular marker”, as used herein, refers to a measurable indicator such as a polypeptide, mRNA, DNA, small molecule. In some embodiments, the molecular marker is a biomarker. In some embodiments, the molecular marker is of at least one type selected from the group of intra, extra-cellular and soluble markers.

The term “pathogen”, as used herein, refers to any infectious pathogen, wherein the pathogen and/or the symptoms of the pathogen infection are capable of inducing a severe respiratory condition. In some embodiments the pathogen is a virus, a bacterium, a fungus, a parasite or a combination thereof. In some embodiments, the pathogen described herein is a pneumonia- inducing pathogen. In some embodiments, the pathogen described herein is a sepsis-inducing pathogen. In some embodiments, the pathogen described herein comprises at least one bacterium selected from the group of Streptococcus pneumoniae, Haemophilus influenzae, Enterobacteriaceae, Staphylococcus aureus, Legionella pneumophila, Chlamydia pneumoniae, Mycoplasma pneumoniae, Pseudomonas aeruginosa, Acinetobacter baumannii and Stenotrophompnas maltophilia. In some embodiments, the pathogen described herein comprises a virus selected from the group of Influenza virus, Rhinovirus, RSV, Parainfluenza virus, Coronavirus, Enterovirus, HSV and CMV. In some embodiments, the pathogen described herein is SARS-CoV-2. In some embodiments, the pathogen described herein comprises Pneumocystis Jirovecii, Mucormycosis and/or Aspergillus fumigatus. In some embodiments, the pathogen described herein comprises Toxoplasma gondii.

The term “patient”, as used herein, refers to an animal, preferably a mammal, more preferably a human. In some embodiments, the patient described herein is a patient with an age above 18, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, or 85. In some embodiments, the patient described herein is a patient with a BMI above 25, 26, 27, 28, 28 or 30. In some embodiments the patient has a body temperature above 37.5, 37.6, 37.8, 37.9, 38.0, 38.1, 38.2, 38.3, 38.4, 38.5, 38.6, 38.7, 38.8, 38.9, 39.0, 39.1, 39.2, 39.3, 39.4 or 39.5. In some embodiments, the patient described herein is a patient with increased blood pressure. In some embodiments, the patient described herein is a patient with an increased heart rate. In some embodiments, the patient described herein is a patient with a decreased respiratory rate.

The term “acute lung injury”, as used herein, refers to a respiratory condition that is characterized by a PaC /FiC ratio of 100 to 300 mmHg and develops within 1 week.

The term “head trauma”, as used herein, refers to any injury that may occur when a mechanical force causes damage to the head. The mechanical force may be internal or external. For example, a head trauma may result when the head suddenly collides with an object, or when an object pierces the skull and enters brain tissue. Alternatively, a head trauma may be caused by an “impulsive” force transmitted to the head from other parts of the body. In some embodiments, the head trauma described herein is a stroke. In some embodiments, the head trauma described herein induces systemic inflammation and/or impairs lung function.

The term “chest trauma”, as used herein, refers to any trauma that affects the chest and/or induces local inflammation in the chest. In some embodiments, the chest trauma described herein is selected from the group of injuries to the chest wall, pulmonary injury, injuries involving the pleural space, injury to the airways, cardiac injury, blood vessel injuries and injuries to structures within the torso. In some embodiments, the chest trauma described herein induces local and/or systemic inflammation and/or impairs lung function.

The term “other trauma”, as used herein, refers to any trauma that (directly or indirectly) impairs lung function and/or inflammation. In some embodiments, the other trauma described herein is selected from the group of accident, surgery, infarct.

The term “pancreatitis”, as used herein, refers to a condition characterized by inflammation of the pancreas. In some embodiments, the pancreatitis described herein is acute pancreatitis. In some embodiments, the pancreatitis described herein is chronic pancreatitis. In some embodiments, the pancreatitis is characterized by more than 2, more than 2.5, more than 3 times higher amylase and/or lipase levels than the norm range.

The term “predictive of a severe respiratory condition”, as used herein, means that the markers are predictive for at least one disease-related parameter and/or treatment-related parameter of a severe respiratory condition. In some embodiments, the disease-related parameter described herein is a clinical disease-state, disease-progression and/or a disease outcome parameter. In some embodiments, the treatment-related parameter is indicative of treatment susceptibility.

The term “severe respiratory condition”, as used herein, refers to any respiratory condition that is characterized by a PaCk/FiCk ratio of less than 300mmHg. In some embodiments, the severe respiratory condition described herein is additionally characterized by at least one characteristic selected from the group of inflammation, bilateral opacities on chest imaging, a positive end-expiratory pressure of more than 5 cm FkO, Ck saturation below 92%, respiratory failure, PaCk/FiCk ratio of less than 200mmHg and PaCk/FiCk ratio of less than lOOmmHg,. In embodiments wherein the patient described herein is suffering from an acute lung injury the severe respiratory condition described herein is additionally characterized by at least one characteristic selected from the group of inflammation, bilateral opacities on chest imaging, a positive end-expiratory pressure of more than 5 cm FkO, Ck saturation below 92%, respiratory failure and PaCk/FiCk ratio of less than lOOmmHg. In some embodiments, the severe respiratory condition described herein comprises at least one condition selected from the group of acute respiratory distress syndrome (ARDS) status, admittance into ICU, requirement of ventilatory management, development of cytokine release syndrome (CRS) and secondary hemophagocytic lymphohistiocytosis (sHLH). In some embodiments, the severe respiratory condition described herein is ARDS, admittance into ICU and/or requirement of ventilatory management.

In some embodiments, the invention relates to a panel of molecular markers, which in combination of at least two markers define a unique state of the immune system in patients infected with a viral and/or bacterial pathogen, patients suffering from acute inhalation injury or patients suffering from head, chest and/or other trauma, wherein the markers are predictive of severe respiratory conditions such as acute respiratory distress syndrome (ARDS), admittance into ICU and/or development of cytokine release syndrome (CRS) and/or secondary hemophagocytic lymphohistiocytosis (sHLH).

The inventors have identified a set of molecular markers, which in combination, define a unique state of the immune system and therefore immune response. The presence of these markers, e.g. in PBMC samples, as determined using e.g. an antibody -based assay such as flow or mass cytometry, is able to identify patients with propensity to develop a severe respiratory condition such as ARDS and is likely to require intensive medical intervention, in patients infected with SARS-CoV-2 and/or other pathogens.

Therefore, the patient described herein may be at risk to develop a severe respiratory condition, such as ARDS. Within the present invention, the patient may be infected with a viral and/or bacterial pathogen. The patient may suffer from acute inhalation injury or be a patient suffering from head, chest and/or other trauma. In some embodiments, the patient described herein has more than one risk factor to develop a severe respiratory condition selected from the group of a) infected with at least one pathogen, b) acute lung injury, c) pancreatitis, and d) head, chest and/or other trauma. Therefore, the patient may for example have pancreatitis and experience a trauma such as surgery, whereby the inflammation extends from the pancreas to the lung.

In some embodiments, the invention relates to the panel of markers of the invention, comprising at least 3, 4, 5, 6, 7, 8, 9, 10 molecular markers.

The inventors found that the combination of at least 3, 4, 5, 6, 7, 8, 9, 10 molecular markers surprisingly increases the accuracy of the prediction of the severe respiratory condition.

In some embodiments, the panel of markers according to the invention comprises at least two markers, preferably three, four, five, six, seven, eight, nine, ten markers or all markers selected from the group of CD163, CD274 (PD-L1), CD326 (EpCAM), CD45RA, CD21, S100A9, CD206, CD209, CD192 (CCR2), CD254, CD32, CD64, CD172a, CD123, CD169, CD86, HLA-DR, CD54, CD33, CD36, CD49a, CD31, gp38, CD80, CD34, CDla, CX3R1, CD195, CD68 and CD 106. The inventors further surprisingly found that depletion of cells expressing CD49a correlates with a high propensity to develop a severe respiratory condition such as ARDS. In contrast, enrichment of cells expressing CD163, CD274 (PD-L1), CD326 (EpCAM), CD45RA, CD21, S100A9, CD206, CD209, CD 192 (CCR2), CD254, CD32, CD64, CD 172a, CD123, CD 169, CD86, HLA-DR, CD54, CD33, CD36, CD31, gp38, CD80, CD34, CDla, CX3R1, CD 195, CD68 and CD 106 correlates with high propensity to develop a severe respiratory condition. Within the present invention, enrichment of cells expressing CD49a may be indicative of a lowered propensity to develop a severe respiratory condition and/or depletion of cells expressing CD163, CD274 (PD-L1), CD326 (EpCAM), CD45RA, CD21, S100A9, CD206, CD209, CD192 (CCR2), CD254, CD32, CD64, CD172a, CD123, CD169, CD86, HLA-DR, CD54, CD33, CD36, CD31, gp38, CD80, CD34, CDla, CX3R1, CD195, CD68 and CD 106 may be indicative of a lowered propensity to develop a severe respiratory condition.

As such, the panel of molecular markers according to the invention comprises two markers, preferably three, four, five, six, seven, eight, nine, ten or more markers or all markers selected from CD64, CD33, CD36, CD169, S100A9, CD31, CD192 (CCR2), CD209, CD254, CD32, CD 172a, HLA-DR, CD86, CD206, CD54, Ki67, Bcl6, CXCR5, CD38, CD49a, CD127 and CD27. The inventors further surprisingly found that depletion of cells expressing CD49a, CD127 and/or CD27 correlates with a high propensity to develop a severe respiratory condition. In contrast, enrichment of cells expressing CD64, CD33, CD36, CD169, S100A9, CD31, CD 192 (CCR2), CD209, CD254, CD32, CD172a, HLA-DR, CD86, CD206, CD54, Ki67, Bcl6, CXCR5, and/or CD38 correlates with high propensity to develop a severe respiratory condition. Within the present invention, enrichment of cells expressing CD49a, CD127 and/or CD27 may be indicative of a lowered propensity to develop a severe respiratory condition and/or depletion of cells expressing CD64, CD33, CD36, CD169, S100A9, CD31, CD 192 (CCR2), CD209, CD254, CD32, CD 172a, HLA-DR, CD86, CD206, CD54, Ki67, Bcl6, CXCR5, and/or CD38 may be indicative of a lowered propensity to develop a severe respiratory condition.

In some embodiments, the panel of markers according to the invention comprises the marker CD 163 and at least one, two, three, four, five, six, seven, eight, nine, ten marker(s) or all markers selected from the group of CD274 (PD-L1), CD326 (EpCAM), CD45RA, CD21, S100A9, CD206, CD209, CD192 (CCR2), CD254, CD32, CD64, CD172a, CD123, CD169, CD86, HLA-DR, CD54, CD33, CD36, CD49a, CD31, gp38, CD80, CD34, CDla, CX3R1, CD 195, CD68 and CD 106.

In some embodiments, the panel of markers according to the invention comprises the marker CD274 (PD-L1) and at least one, two, three, four, five, six, seven, eight, nine, ten marker(s) or all markers selected from the group of CD163, CD326 (EpCAM), CD45RA, CD21, S100A9, CD206, CD209, CD192 (CCR2), CD254, CD32, CD64, CD172a, CD123, CD169, CD86, HLA-DR, CD54, CD33, CD36, CD49a, CD31, gp38, CD80, CD34, CDla, CX3R1, CD195, CD68 and CD 106.

In some embodiments, the panel of markers according to the invention comprises the marker CD326 (EpCAM) and at least one, two, three, four, five, six, seven, eight, nine, ten marker(s) or all markers selected from the group of CD 163, CD274 (PD-L1), CD45RA, CD21, S100A9, CD206, CD209, CD192 (CCR2), CD254, CD32, CD64, CD172a, CD123, CD169, CD86, HLA-DR, CD54, CD33, CD36, CD49a, CD31, gp38, CD80, CD34, CDla, CX3R1, CD195, CD68 and CD 106.

In some embodiments, the panel of markers according to the invention comprises the marker CD45RA and at least one, two, three, four, five, six, seven, eight, nine, ten marker(s) or all markers selected from the group of CD163, CD274 (PD-L1), CD326 (EpCAM), CD21, S100A9, CD206, CD209, CD192 (CCR2), CD254, CD32, CD64, CD172a, CD123, CD169, CD86, HLA-DR, CD54, CD33, CD36, CD49a, CD31, gp38, CD80, CD34, CDla, CX3R1, CD 195, CD68 and CD 106.

In some embodiments, the panel of markers according to the invention comprises the marker CD21 and at least one, two, three, four, five, six, seven, eight, nine, ten marker(s) or all markers selected from the group of CD163, CD274 (PD-L1), CD326 (EpCAM), CD45RA, S100A9, CD206, CD209, CD192 (CCR2), CD254, CD32, CD64, CD172a, CD123, CD169, CD86, HLA-DR, CD54, CD33, CD36, CD49a, CD31, gp38, CD80, CD34, CDla, CX3R1, CD195, CD68 and CD 106.

In some embodiments, the panel of markers according to the invention comprises the marker S100A9 and at least one, two, three, four, five, six, seven, eight, nine, ten marker(s) or all markers selected from the group of CD163, CD274 (PD-L1), CD326 (EpCAM), CD45RA, CD21, CD206, CD209, CD192 (CCR2), CD254, CD32, CD64, CD172a, CD123, CD169, CD86, HLA-DR, CD54, CD33, CD36, CD49a, CD31, gp38, CD80, CD34, CDla, CX3R1, CD 195, CD68 and CD 106.

In some embodiments, the panel of markers according to the invention comprises the marker CD206 and at least one, two, three, four, five, six, seven, eight, nine, ten marker(s) or all markers selected from the group of CD163, CD274 (PD-L1), CD326 (EpCAM), CD45RA, CD21, S100A9, CD209, CD192 (CCR2), CD254, CD32, CD64, CD172a, CD123, CD169, CD86, HLA-DR, CD54, CD33, CD36, CD49a, CD31, gp38, CD80, CD34, CDla, CX3R1, CD 195, CD68 and CD 106.

In some embodiments, the panel of markers according to the invention comprises the marker CD209 and at least one, two, three, four, five, six, seven, eight, nine, ten marker(s) or all markers selected from the group of CD163, CD274 (PD-L1), CD326 (EpCAM), CD45RA, CD21, S100A9, CD206, CD192 (CCR2), CD254, CD32, CD64, CD172a, CD123, CD169, CD86, HLA-DR, CD54, CD33, CD36, CD49a, CD31, gp38, CD80, CD34, CDla, CX3R1, CD 195, CD68 and CD 106.

In some embodiments, the panel of markers according to the invention comprises the marker CD 192 (CCR2) and at least one, two, three, four, five, six, seven, eight, nine, ten marker(s) or all markers selected from the group of CD163, CD274 (PD-L1), CD326 (EpCAM), CD45RA, CD21, S100A9, CD206, CD209, CD254, CD32, CD64, CD172a, CD123, CD169, CD86, HLA-DR, CD54, CD33, CD36, CD49a, CD31, gp38, CD80, CD34, CDla, CX3R1, CD195, CD68 and CD 106.

In some embodiments, the panel of markers according to the invention comprises the marker CD254 and at least one, two, three, four, five, six, seven, eight, nine, ten marker(s) or all markers selected from the group of CD163, CD274 (PD-L1), CD326 (EpCAM), CD45RA, CD21, S100A9, CD206, CD209, CD192 (CCR2), CD32, CD64, CD172a, CD123, CD169, CD86, HLA-DR, CD54, CD33, CD36, CD49a, CD31, gp38, CD80, CD34, CDla, CX3R1, CD 195, CD68 and CD 106.

In some embodiments, the panel of markers according to the invention comprises the marker CD32 and at least one, two, three, four, five, six, seven, eight, nine, ten marker(s) or all markers selected from the group of CD163, CD274 (PD-L1), CD326 (EpCAM), CD45RA, CD21, S100A9, CD206, CD209, CD 192 (CCR2), CD254, CD64, CD 172a, CD 123, CD 169, CD86, HLA-DR, CD54, CD33, CD36, CD49a, CD31, gp38, CD80, CD34, CDla, CX3R1, CD195, CD68 and CD 106.

In some embodiments, the panel of markers according to the invention comprises the marker CD64 and at least one, two, three, four, five, six, seven, eight, nine, ten marker(s) or all markers selected from the group of CD163, CD274 (PD-L1), CD326 (EpCAM), CD45RA, CD21, S100A9, CD206, CD209, CD192 (CCR2), CD254, CD32, CD172a, CD123, CD169, CD86, HLA-DR, CD54, CD33, CD36, CD49a, CD31, gp38, CD80, CD34, CDla, CX3R1, CD195, CD68 and CD 106.

In some embodiments, the panel of markers according to the invention comprises the marker CD 172a and at least one, two, three, four, five, six, seven, eight, nine, ten marker(s) or all markers selected from the group of CD163, CD274 (PD-L1), CD326 (EpCAM), CD45RA, CD21, S100A9, CD206, CD209, CD192 (CCR2), CD254, CD32, CD64, CD123, CD169, CD86, HLA-DR, CD54, CD33, CD36, CD49a, CD31, gp38, CD80, CD34, CDla, CX3R1, CD 195, CD68 and CD 106.

In some embodiments, the panel of markers according to the invention comprises the marker CD 123 and at least one, two, three, four, five, six, seven, eight, nine, ten marker(s) or all markers selected from the group of CD163, CD274 (PD-L1), CD326 (EpCAM), CD45RA, CD21,S100A9, CD206, CD209, CD 192 (CCR2), CD254, CD32, CD64, CD 172a, CD 169, CD86, HLA-DR, CD54, CD33, CD36, CD49a, CD31, gp38, CD80, CD34, CDla, CX3R1, CD 195, CD68 and CD 106.

In some embodiments, the panel of markers according to the invention comprises the marker CD 169 and at least one, two, three, four, five, six, seven, eight, nine, ten marker(s) or all markers selected from the group of CD163, CD274 (PD-L1), CD326 (EpCAM), CD45RA, CD21, S100A9, CD206, CD209, CD192 (CCR2), CD254, CD32, CD64, CD172a, CD123, CD86, HLA-DR, CD54, CD33, CD36, CD49a, CD31, gp38, CD80, CD34, CDla, CX3R1, CD 195, CD68 and CD 106.

In some embodiments, the panel of markers according to the invention comprises the marker CD86 and at least one, two, three, four, five, six, seven, eight, nine, ten marker(s) or all markers selected from the group of CD163, CD274 (PD-L1), CD326 (EpCAM), CD45RA, CD21, S100A9, CD206, CD209, CD192 (CCR2), CD254, CD32, CD64, CD172a, CD123, CD169, CD86, HLA-DR, CD54, CD33, CD36, CD49a, CD31, gp38, CD80, CD34, CDla, CX3R1, CD 195, CD68 and CD 106.

In some embodiments, the panel of markers according to the invention comprises the marker HLA-DR and at least one, two, three, four, five, six, seven, eight, nine, ten marker(s) or all markers selected from the group of CD163, CD274 (PD-L1), CD326 (EpCAM), CD45RA, CD21, S100A9, CD206, CD209, CD192 (CCR2), CD254, CD32, CD64, CD172a, CD123, CD 169, CD86, CD54, CD33, CD36, CD49a, CD31, gp38, CD80, CD34, CDla, CX3R1, CD 195, CD68 and CD 106.

In some embodiments, the panel of markers according to the invention comprises the marker CD54 and at least one, two, three, four, five, six, seven, eight, nine, ten marker(s) or all markers selected from the group of CD163, CD274 (PD-L1), CD326 (EpCAM), CD45RA, CD21, S100A9, CD206, CD209, CD192 (CCR2), CD254, CD32, CD64, CD172a, CD123, CD169, CD86, HLA-DR, CD33, CD36, CD49a, CD31, gp38, CD80, CD34, CDla, CX3R1, CD195, CD68 and CD 106.

In some embodiments, the panel of markers according to the invention comprises the marker CD33 and at least one, two, three, four, five, six, seven, eight, nine, ten marker(s) or all markers selected from the group of CD163, CD274 (PD-L1), CD326 (EpCAM), CD45RA, CD21, S100A9, CD206, CD209, CD192 (CCR2), CD254, CD32, CD64, CD172a, CD123, CD169, CD86, HLA-DR, CD54, CD36, CD49a, CD31, gp38, CD80, CD34, CDla, CX3R1, CD195, CD68 and CD 106.

In some embodiments, the panel of markers according to the invention comprises the marker CD36 and at least one, two, three, four, five, six, seven, eight, nine, ten marker(s) or all markers selected from the group of CD163, CD274 (PD-L1), CD326 (EpCAM), CD45RA, CD21, S100A9, CD206, CD209, CD192 (CCR2), CD254, CD32, CD64, CD172a, CD123, CD169, CD86, HLA-DR, CD54, CD33, CD49a, CD31, gp38, CD80, CD34, CDla, CX3R1, CD195, CD68 and CD 106.

In some embodiments, the panel of markers according to the invention comprises the marker CD49a and at least one, two, three, four, five, six, seven, eight, nine, ten marker(s) or all markers selected from the group of CD163, CD274 (PD-L1), CD326 (EpCAM), CD45RA, CD21, S100A9, CD206, CD209, CD192 (CCR2), CD254, CD32, CD64, CD172a, CD123, CD 169, CD86, HLA-DR, CD54, CD33, CD36, CD31, gp38, CD80, CD34, CDla, CX3R1, CD 195, CD68 and CD 106.

In some embodiments, the panel of markers according to the invention comprises the marker CD31 and at least one, two, three, four, five, six, seven, eight, nine, ten marker(s) or all markers selected from the group of CD163, CD274 (PD-L1), CD326 (EpCAM), CD45RA, CD21, S100A9, CD206, CD209, CD192 (CCR2), CD254, CD32, CD64, CD172a, CD123, CD169, CD86, HLA-DR, CD54, CD33, CD36, CD49a, gp38, CD80, CD34, CDla, CX3R1, CD195, CD68 and CD 106.

In some embodiments, the panel of markers according to the invention comprises the marker gp38 and at least one, two, three, four, five, six, seven, eight, nine, ten marker(s) or all markers selected from the group of CD163, CD274 (PD-L1), CD326 (EpCAM), CD45RA, CD21, S100A9, CD206, CD209, CD192 (CCR2), CD254, CD32, CD64, CD172a, CD123, CD169, CD86, HLA-DR, CD54, CD33, CD36, CD49a, CD31, CD80, CD34, CDla, CX3R1, CD195, CD68 and CD 106.

In some embodiments, the panel of markers according to the invention comprises the marker CD80 and at least one, two, three, four, five, six, seven, eight, nine, ten marker(s) or all markers selected from the group of CD163, CD274 (PD-L1), CD326 (EpCAM), CD45RA, CD21, S100A9, CD206, CD209, CD192 (CCR2), CD254, CD32, CD64, CD172a, CD123, CD169, CD86, HLA-DR, CD54, CD33, CD36, CD49a, CD31, gp38, CD34, CDla, CX3R1, CD195, CD68 and CD 106.

In some embodiments, the panel of markers according to the invention comprises the marker CD34 and at least one, two, three, four, five, six, seven, eight, nine, ten marker(s) or all markers selected from the group of CD163, CD274 (PD-L1), CD326 (EpCAM), CD45RA, CD21, S100A9, CD206, CD209, CD192 (CCR2), CD254, CD32, CD64, CD172a, CD123, CD169, CD86, HLA-DR, CD54, CD33, CD36, CD49a, CD31, gp38, CD80, CDla, CX3R1, CD195, CD68 and CD 106.

In some embodiments, the panel of markers according to the invention comprises the marker CDla and at least one, two, three, four, five, six, seven, eight, nine, ten marker(s) or all markers selected from the group of CD163, CD274 (PD-L1), CD326 (EpCAM), CD45RA, CD21, S100A9, CD206, CD209, CD192 (CCR2), CD254, CD32, CD64, CD172a, CD123, CD169, CD86, HLA-DR, CD54, CD33, CD36, CD49a, CD31, gp38, CD80, CD34, CX3R1, CD195, CD68 and CD 106.

In some embodiments, the panel of markers according to the invention comprises the marker CX3R1 and at least one, two, three, four, five, six, seven, eight, nine, ten marker(s) or all markers selected from the group of CD163, CD274 (PD-L1), CD326 (EpCAM), CD45RA, CD21, S100A9, CD206, CD209, CD192 (CCR2), CD254, CD32, CD64, CD172a, CD123, CD 169, CD86, HLA-DR, CD54, CD33, CD36, CD49a, CD31, gp38, CD80, CD34, CDla, CD 195, CD68 and CD 106.

In some embodiments, the panel of markers according to the invention comprises the marker CD 195, and at least one, two, three, four, five, six, seven, eight, nine, ten marker(s) or all markers selected from the group of CD163, CD274 (PD-L1), CD326 (EpCAM), CD45RA, CD21, S100A9, CD206, CD209, CD192 (CCR2), CD254, CD32, CD64, CD172a, CD123, CD 169, CD86, HLA-DR, CD54, CD33, CD36, CD49a, CD31, gp38, CD80, CD34, CDla, CX3R1, CD68 and CD 106.

In some embodiments, the panel of markers according to the invention comprises the marker CD68 and at least one, two, three, four, five, six, seven, eight, nine, ten marker(s) or all markers selected from the group of CD163, CD274 (PD-L1), CD326 (EpCAM), CD45RA, CD21, S100A9, CD206, CD209, CD192 (CCR2), CD254, CD32, CD64, CD172a, CD123, CD169, CD86, HLA-DR, CD54, CD33, CD36, CD49a, gp38, CD80, CD34, CDla, CX3R1, CD195, CD31 and CD 106.

In some embodiments, the panel of markers according to the invention comprises the marker CD 106 and at least one, two, three, four, five, six, seven, eight, nine, ten marker(s) or all markers selected from the group of CD163, CD274 (PD-L1), CD326 (EpCAM), CD45RA, CD21, S100A9, CD206, CD209, CD192 (CCR2), CD254, CD32, CD64, CD172a, CD123, CD 169, CD86, HLA-DR, CD54, CD33, CD36, CD49a, gp38, CD80, CD34, CDla, CX3R1, CD195, CD31 and CD68.

The term “cell-surface markers”, as used herein, refers to markers from Table 1. The cell- surface markers do not necessarily need to be expressed at the cell surface. For example the cell surface markers may also be expressed inside the cell in the cell membrane.

Accordingly, the invention is at least in part based on the finding that certain cell-surface markers and/or certain cell-surface marker combinations contribute particularly to the prediction of severe respiratory conditions.

In some embodiments, the panel of markers of the invention further comprises at least one soluble marker.

In some embodiments, the invention relates to the panel of markers of the invention further comprising one or more molecular markers selected from the group consisting of PCT, CRP, G-CSF, GM-CSF, CXCL1, IFNg, IL-lra, IL-lb, CXCL8, IL-10, IL12p70, IL-18, IL-6, CXCL10, CCL2, CCL22, CXCL9, TNFa and VEGFa.

These markers have been shown to surprisingly refine the definition of the state of the immune system of a patient infected with a viral and/or bacterial pathogen. In contrast to the markers CD163, CD274 (PD-L1), CD326 (EpCAM), CD45RA, CD21, S100A9, CD206, CD209, CD 192 (CCR2), CD254, CD32, CD64, CD172a, CD123, CD169, CD86, HLA-DR, CD54, CD33, CD36, CD49a, CD106, gp38, CD80, CD34, CDla, CX3R1, CD195, CD31 and CD68 the markers PCT, CRP, G-CSF, GM-CSF, CXCL1, IFNg, IL-lra, IL-lb, CXCL8, IL-10, IL12p70, IL-18, IL-6, CXCL10, CCL2, CCL22, CXCL9, TNFa and VEGFa are present in the serum and/or plasma of a patient.

In some embodiments, the panel of markers according to the invention comprises the cell- surface markers described herein, the marker PCT and preferably at least 1, 2, 3, 4, 5, 6, 7 additional marker selected from the group of CRP, G-CSF, GM-CSF, CXCL1, IFNg, IL-lra, IL-lb, CXCL8, IL-10, IL12p70, IL-18, IL-6, CXCL10, CCL2, CCL22, CXCL9, TNFa and VEGFa. In some embodiments, the panel of markers according to the invention comprises the cell-surface markers described herein, the marker CRP and preferably at least 1, 2, 3, 4, 5, 6, 7 additional marker selected from the group of PCT, G-CSF, GM-CSF, CXCL1, IFNg, IL-lra, IL-lb, CXCL8, IL-10, IL12p70, IL-18, IL-6, CXCL10, CCL2, CCL22, CXCL9, TNFa and VEGFa.

In some embodiments, the panel of markers according to the invention comprises the cell- surface markers described herein, the marker G-CSF and preferably at least 1, 2, 3, 4, 5, 6, 7 additional marker selected from the group of PCT, CRP, GM-CSF, CXCL1, IFNg, IL-lra, IL- lb, CXCL8, IL-10, IL12p70, IL-18, IL-6, CXCL10, CCL2, CCL22, CXCL9, TNFa and VEGFa.

In some embodiments, the panel of markers according to the invention comprises the cell- surface markers described herein, the marker GM-CSF and preferably at least 1, 2, 3, 4, 5, 6, 7 additional marker selected from the group of PCT, CRP, G-CSF, CXCL1, IFNg, IL-lra, IL- lb, CXCL8, IL-10, IL12p70, IL-18, IL-6, CXCL10, CCL2, CCL22, CXCL9, TNFa and VEGFa.

In some embodiments, the panel of markers according to the invention comprises the cell- surface markers described herein, the marker CXCL1 and preferably at least 1, 2, 3, 4, 5, 6, 7 additional marker selected from the group of PCT, CRP, G-CSF, GM-CSF, IFNg, IL-lra, IL- lb, CXCL8, IL-10, IL12p70, IL-18, IL-6, CXCL10, CCL2, CCL22, CXCL9, TNFa and VEGFa.

In some embodiments, the panel of markers according to the invention comprises the cell- surface markers described herein, the marker IFNg and preferably at least 1, 2, 3, 4, 5, 6, 7 additional marker selected from the group of PCT, CRP, G-CSF, GM-CSF, CXCL1, IL-lra, IL-lb, CXCL8, IL-10, IL12p70, IL-18, IL-6, CXCL10, CCL2, CCL22, CXCL9, TNFa and VEGFa.

In some embodiments, the panel of markers according to the invention comprises the cell- surface markers described herein, the marker IL-lra and preferably at least 1, 2, 3, 4, 5, 6, 7 additional marker selected from the group of PCT, CRP, G-CSF, GM-CSF, CXCL1, IFNg, IL- lb, CXCL8, IL-10, IL12p70, IL-18, IL-6, CXCL10, CCL2, CCL22, CXCL9, TNFa and VEGFa.

In some embodiments, the panel of markers according to the invention comprises the cell- surface markers described herein, the marker IL-lb and preferably at least 1, 2, 3, 4, 5, 6, 7 additional marker selected from the group of PCT, CRP, G-CSF, GM-CSF, CXCL1, IFNg, IL- lra, CXCL8, IL-10, IL12p70, IL-18, IL-6, CXCL10, CCL2, CCL22, CXCL9, TNFa and VEGFa.

In some embodiments, the panel of markers according to the invention comprises the cell- surface markers described herein, the marker CXCL8 and preferably at least 1, 2, 3, 4, 5, 6, 7 additional marker selected from the group of PCT, CRP, G-CSF, GM-CSF, CXCL1, IFNg, IL- lra, IL-lb, IL-10, IL12p70, IL-18, IL-6, CXCL10, CCL2, CCL22, CXCL9, TNFa and VEGFa.

In some embodiments, the panel of markers according to the invention comprises the cell- surface markers described herein, the marker IL-10 and preferably at least 1, 2, 3, 4, 5, 6, 7 additional marker selected from the group of PCT, CRP, G-CSF, GM-CSF, CXCL1, IFNg, IL- lra, IL-lb, CXCL8, IL12p70, IL-18, IL-6, CXCL10, CCL2, CCL22, CXCL9, TNFa and VEGFa.

In some embodiments, the panel of markers according to the invention comprises the cell- surface markers described herein, the marker IL12p70 and preferably at least 1, 2, 3, 4, 5, 6, 7 additional marker selected from the group of PCT, CRP, G-CSF, GM-CSF, CXCL1, IFNg, IL- lra, IL-lb, CXCL8, IL-10, IL-18, IL-6, CXCL10, CCL2, CCL22, CXCL9, TNFa and VEGFa.

In some embodiments, the panel of markers according to the invention comprises the cell- surface markers described herein, the marker IL-18 and preferably at least 1, 2, 3, 4, 5, 6, 7 additional marker selected from the group of PCT, CRP, G-CSF, GM-CSF, CXCL1, IFNg, IL- lra, IL-lb, CXCL8, IL-10, IL12p70, IL-6, CXCL10, CCL2, CCL22, CXCL9, TNFa and VEGFa.

In some embodiments, the panel of markers according to the invention comprises the cell- surface markers described herein, the marker IL-6 and preferably at least 1, 2, 3, 4, 5, 6, 7 additional marker selected from the group of PCT, CRP, G-CSF, GM-CSF, CXCL1, IFNg, IL- lra, IL-lb, CXCL8, IL-10, IL12p70, IL-18, CXCL10, CCL2, CCL22, CXCL9, TNFa and VEGFa.

In some embodiments, the panel of markers according to the invention comprises the cell- surface markers described herein, the marker CXCL10 and preferably at least 1, 2, 3, 4, 5, 6, 7 additional marker selected from the group of PCT, CRP, G-CSF, GM-CSF, CXCL1, IFNg, IL- lra, IL-lb, CXCL8, IL-10, IL12p70, IL-18, IL-6, CCL2, CCL22, CXCL9, TNFa and VEGFa.

In some embodiments, the panel of markers according to the invention comprises the cell- surface markers described herein, the marker CCL2 and preferably at least 1, 2, 3, 4, 5, 6, 7 additional marker selected from the group of PCT, CRP, G-CSF, GM-CSF, CXCL1, IFNg, IL- lra, IL-lb, CXCL8, IL-10, IL12p70, IL-18, IL-6, CXCL10, CCL22, CXCL9, TNFa and VEGFa.

In some embodiments, the panel of markers according to the invention comprises the cell- surface markers described herein, the marker CCL22 and preferably at least 1, 2, 3, 4, 5, 6, 7 additional marker selected from the group of PCT, CRP, G-CSF, GM-CSF, CXCL1, IFNg, IL- lra, IL-lb, CXCL8, IL-10, IL12p70, IL-18, IL-6, CXCL10, CCL2, CXCL9, TNFa and VEGFa.

In some embodiments, the panel of markers according to the invention comprises the cell- surface markers described herein, the marker CXCL9 and preferably at least 1, 2, 3, 4, 5, 6, 7 additional marker selected from the group of PCT, CRP, G-CSF, GM-CSF, CXCL1, IFNg, IL- lra, IL-lb, CXCL8, IL-10, IL12p70, IL-18, IL-6, CXCL10, CCL2, CCL22, TNFa and VEGFa.

In some embodiments, the panel of markers according to the invention comprises the cell- surface markers described herein, the marker TNFa and preferably at least 1, 2, 3, 4, 5, 6, 7 additional marker selected from the group of PCT, CRP, G-CSF, GM-CSF, CXCL1, IFNg, IL- lra, IL-lb, CXCL8, IL-10, IL12p70, IL-18, IL-6, CXCL10, CCL2, CCL22, CXCL9 and VEGFa.

In some embodiments, the panel of markers according to the invention comprises the cell- surface markers described herein, the marker VEGFa and preferably at least 1, 2, 3, 4, 5, 6, 7 additional marker selected from the group of PCT, CRP, G-CSF, GM-CSF, CXCL1, IFNg, IL- lra, IL-lb, CXCL8, IL-10, IL12p70, IL-18, IL-6, CXCL10, CCL2, CCL22, CXCL9 and TNFa.

In some embodiments, the panel of markers according to the invention comprises the cell- surface markers described herein, the marker PCT, the marker CRP, and preferably at least 1, 2, 3, 4, 5, 6, 7 additional marker selected from the group of G-CSF, GM-CSF, CXCL1, IFNg, IL-lra, IL-lb, CXCL8, IL-10, IL12p70, IL-18, IL-6, CXCL10, CCL2, CCL22, CXCL9, TNFa and VEGFa.

In a preferred embodiment of the invention, the panel further comprises one or more markers selected from the group consisting of G-CSF, GM-CSF, CXCL1, IFNg, IL-lra, IL-lb, CXCL8, IL-10, IL12p70, IL-18, IL-6, CXCL10, CCL2, CCL22, CXCL9, TNFa and VEGFa. These markers have been shown to surprisingly refine the definition of the state of the immune system of a patient infected with a viral and/or bacterial pathogen. In contrast to the markers CD64, CD33, CD36, CD169, S100A9, CD31, CD192 (CCR2), CD209, CD254, CD32, CD172a, HLA-DR, CD86, CD206, CD54, Ki67, Bcl6, CXCR5, CD38, CD49a, CD127 and CD27, the markers G-CSF, GM-CSF, CXCL1, IFNg, IL-lra, IL-lb, CXCL8, IL-10, IL12p70, IL-18, IL- 6, CXCL10, CCL2, CCL22, CXCL9, TNFa and VEGFa are present in the serum and/or plasma of a patient. It was surprisingly found that the state defined by cell-based markers can be refined by using plasma/serum based markers indicative of the respective state of the immune system.

Accordingly, the invention is at least in part based on the finding that certain soluble markers and/or cell-surface markers contribute particularly to the prediction of severe respiratory conditions.

In some embodiments, the invention relates to the panel of markers according to the invention, wherein the panel additionally comprises one or more cell lineage molecular marker(s), in particular one or more marker(s) selected from the group consisting of CD45, CD3, CD14, CD16, CD19, CDl lc, CDl lb, and CD20. In some embodiments, the invention relates to the panel of markers according to the invention, wherein the panel additionally comprises CD45.

In some embodiments, the invention relates to the panel of markers according to the invention, wherein the panel additionally comprises CD3.

In some embodiments, the invention relates to the panel of markers according to the invention, wherein the panel additionally comprises CD14.

In some embodiments, the invention relates to the panel of markers according to the invention, wherein the panel additionally comprises CD 16.

In some embodiments, the invention relates to the panel of markers according to the invention, wherein the panel additionally comprises CD 19. In some embodiments, the invention relates to the panel of markers according to the invention, wherein the panel additionally comprises CD20.

In some embodiments, the invention relates to the panel of markers according to the invention, wherein the panel additionally comprises CD1 lc.

In some embodiments, the invention relates to the panel of markers according to the invention, wherein the panel additionally comprises CD1 lb.

In a further embodiment, the panel additionally comprises one or more cell lineage marker(s), in particular one or more marker(s) selected from the group consisting of CD3, CD4, CD8, CDl lb, CDl lc, CD 14, CD19, CD20, CD45 and CD56.

These cell lineage markers can be used to further refine the cell population targeted by the panel of biomarkers by, for example, ruling out false-negatives.

In a preferred embodiment of the invention, the panel comprises a combination of 3, 4, 5, 6, 7, 8, 9, 10 or more markers. It is preferred that substantially all of CD64, CD33, CD36, CD169, S100A9, CD31, CD 192 (CCR2), CD209, CD254, CD32, CD 172a, HLA-DR, CD86, CD206, CD54, Ki67, Bcl6, CXCR5, CD38, CD49a, CD127 and CD27 are part of the panel of markers. It is more preferred that one or more, preferably substantially all of G-CSF, GM-CSF, CXCL1, IFNg, IL-lra, IL-lb, CXCL8, IL-10, IL12p70, IL-18, IL-6, CXCL10, CCL2, CCL22, CXCL9, TNFa and VEGFa are additionally part of the panel of biomarkers. It is further preferred that one or more, preferably substantially all of CD3, CD4, CD8, CDl lb, CDl lc, CD14, CD19, CD20, CD45 and CD56 are part of the panel of markers. In some embodiments, the panel of markers described herein comprises at least 1, 2, 3, 4, 5, 6, 7, 8, 9 or 10 cell surface markers, at least 1, 2, 3 , 4, 5, 6 or 7 soluble markers and at least 1, 2, 3, 4 or 5 cell lineage markers (Table 4).

CelMineage markers can be used to limit the analysis to cells positive for such markers. Since these markers are known to be specific for certain types of cells, such cell types can be specifically selected for analysis using the panel of markers specific for predicting severe respiratory conditions such as acute respiratory distress syndrome (ARDS), admittance into ICU and/or development of cytokine release syndrome (CRS) and/or secondary hemophagocytic lymphohistiocytosis (sHLH) in a patient infected with a viral and/or bacterial pathogen, patients suffering from acute inhalation injury or patients suffering from head, chest and/or other trauma.

In a further embodiment, the invention relates to a method for defining a unique state of the immune system, the method comprising determining the levels of expression of two or more molecular markers in a plurality of cells and/or levels of proteins and/or analyte in serum and/or plasma, which markers are predictive in patients infected with viral and/or bacterial pathogens, patients suffering from acute inhalation injury or patients suffering from head, chest and/or other trauma of Severe respiratory conditions such ARDS, admittance into ICU and/or development of cytokine release syndrome (CRS) and/or secondary hemophagocytic lymphohistiocytosis (sHLH).

In some embodiments, the at least two markers are selected from the group consisting of CD163, CD274 (PD-L1), CD326 (EpCAM), CD45RA, CD21, S100A9, CD206, CD209, CD 192 (CCR2), CD254, CD32, CD64, CD172a, CD123, CD169, CD86, HLA-DR, CD54, CD33, CD36, CD49a, CD31, gp38, CD80, CD34, CDla, CX3R1, CD195, CD68 and CD106. In some embodiments, the method further comprises determining the levels of expression of one or more markers selected from the group consisting of PCT, CRP, G-CSF, GM-CSF, CXCL1, IFNg, IL-lra, IL-lb, CXCL8, IL-10, IL12p70, IL-18, IL-6, CXCL10, CCL2, CCL22, CXCL9, TNFa and VEGFa.

The method of the invention may further comprise determining the levels of expression of one or more cell lineage marker(s), in particular one or more marker(s) selected from the group consisting of CD45, CD3, CD14, CD16, CD19, CDl lb, CDl lc and CD20.

In some embodiments, the levels of expression of more than two markers are determined within the methods of the invention. In some embodiments, the levels of expression of 3, 4, 5, 6, 7, 8, 9, 10 or more markers are determined. It is preferred that the levels of expression of substantially all of CD163, CD274 (PD-L1), CD326 (EpCAM), CD45RA, CD21,S100A9, CD206, CD209, CD192 (CCR2), CD254, CD32, CD64, CD172a, CDl lc, CD123, CD169, CD86, HLA-DR, CDl lb, CD54, CD33, CD36, CD49a, CD31, gp38, CD80, CD34, CDla, CX3R1, CD 195, CD68 and CD 106 are determined within the methods of the present invention. In some embodiments, one or more, e.g. substantially all of the levels of expression of PCT, CRP, G-CSF, GM-CSF, CXCL1, IFNg, IL-lra, IL-lb, CXCL8, IL-10, IL12p70, IL-18, IL-6, CXCL10, CCL2, CCL22, CXCL9, TNFa and VEGFa are additionally determined within the methods of the invention. In some embodiments, one or more, e.g. substantially all of the levels of expression of CD45, CD3, CD14, CD16, CD19, and CD20 are determined within the methods of the invention. It is preferred that the at least two markers are selected from the group consisting of CD64, CD33, CD36, CD169, S100A9, CD31, CD192 (CCR2), CD209, CD254, CD32, CD 172a, HLA-DR, CD86, CD206, CD54, Ki67, Bcl6, CXCR5, CD38, CD49a, CD127 and CD27. It is preferred that the method further comprises determining the levels of expression of one or more markers selected from the group consisting of G-CSF, GM-CSF, CXCL1, IFNg, IL-lra, IL-lb, CXCL8, IL-10, IL12p70, IL-18, IL-6, CXCL10, CCL2, CCL22, CXCL9, TNFa and VEGFa.

The method of the invention may further comprise determining the levels of expression of one or more cell lineage marker(s), in particular one or more marker(s) selected from the group consisting of CD3, CD4, CD8, CDl lb, CDl lc, CD14, CD19, CD20, CD45 and CD56.

It is preferred that the levels of expression of more than two markers are determined within the methods of the invention. In particular, it is preferred that the levels of expression of 3, 4, 5, 6, 7, 8, 9, 10 or more markers are determined. It is preferred that the levels of expression of substantially all of CD64, CD33, CD36, CD169, S100A9, CD31, CD192 (CCR2), CD209, CD254, CD32, CD 172a, HLA-DR, CD86, CD206, CD54, Ki67, Bcl6, CXCR5, CD38, CD49a, CD127 and CD27 are determined within the methods of the present invention. It is more preferred that, one or more, preferably substantially all of the levels of expression of G-CSF, GM-CSF, CXCL1, IFNg, IL-lra, IL-lb, CXCL8, IL-10, IL12p70, IL-18, IL-6, CXCL10, CCL2, CCL22, CXCL9, TNFa and VEGFa are additionally determined within the methods of the invention. It is further preferred that one or more, preferably substantially all of the levels of expression of CD3, CD4, CD8, CDl lb, CDl lc, CD14, CD19, CD20, CD45 and CD56 are determined within the methods of the invention.

The inventors have surprisingly found that the presence of defined cytokines and/or other molecular markers in the plasma, in combination with the presence of specific immune cells as defined by the panel of the invention can further increase the predictive power, sensitivity/specificity of the method of the invention.

In some embodiments, the invention relates to the panel of markers according to the invention additionally comprising at least one non-molecular marker.

The term “non-molecular marker”, as used herein, refers to any marker that is not represented by a single molecule. In some embodiments, the non-molecular marker described herein is a clinical parameter. In some embodiments, the non-molecular marker described herein is a risk factor for a severe respiratory condition. In some embodiments, the non-molecular marker described herein is at least one marker selected from the group of age, BMI, body temperature, systolic blood pressure, diastolic blood pressure, mean arterial blood pressure, heart rate and respiratory rate. The inventors have surprisingly found that non-molecular markers and/or the molecular markers in the context of non-molecular markers the panel of markers of the invention can further increase the predictive power, sensitivity/specificity of the method of the invention.

In some embodiments, the invention relates to a method for defining a unique state of an immune system, the method comprising the steps of: a) determining the level(s) of expression and/or level(s) of protein of the molecular markers of the panel of markers according to the invention in at least one cell, plasma and/or serum; and b) defining a unique state of the immune system based on the levels of expression and/or level(s) of protein of step (a).

The unique state of the immune system described herein can for example be indicative of an immune system of a subject or of properties of at least one immune cell. Accordingly, the method of the invention can be used to describe states of immune systems that were not described before.

In some embodiments, the invention relates to the method according to the invention, wherein the levels of expression of the molecular markers are determined using an antibody-based assay, in particular wherein the antibody-based assay is an antibody-based flow cytometry or mass cytometry assay.

In some embodiments, the invention relates to the method according to the invention, wherein step (a) additionally comprises retrieving the non-molecular markers of the panel of markers according to the invention and wherein defining a unique state of the immune system in step (b) is additionally based on the retrieved non-molecular markers of step (a).

Accordingly, the method of the invention can be used to describe states of the immune system in relation to non-molecular markers that were not described before.

The method of the invention can also be used for quantifying the frequency of human immune cells in a plurality of cells.

In some embodiments, the invention relates to a method for quantifying the frequency of disease-associated immune cells in a plurality of human cells, the method comprising the steps of: i) defining a unique state of an immune system in a plurality of human cells according to the method according to the invention; and ii) quantifying the frequency of disease-associated immune cells in the plurality of human cells based on the unique state of the immune system.

Such methods can comprise determining the levels of expression of two or more molecular markers in a plurality of circulating cells, which markers are predictive of severe respiratory conditions such as ARDS, admittance into ICU and/or development of cytokine release syndrome (CRS) and/or secondary hemophagocytic lymphohistiocytosis (sHLH). Preferably, the markers as provided herein are used. In the present invention, the quantification may additionally comprise setting a threshold or gate on the level of one or more of the molecular markers to define cells defined as positively expressing one or more of the markers. The relation of cells defined as positively expressing one or more of the markers versus the total number of cells in the sample may be used to determine the relative frequency of such cells in the sample. The above may also be used for markers that are depleted.

It is preferred that the human immune cells are immune cells from the myeloid lineage and/or the lymphoid lineage. Preferred cells include circulating cells. Within the invention PBMCs are also preferred to be used. The skilled person is aware how such cells can be obtained from a patient.

In some embodiments, the invention relates to the method according to the invention, wherein (I) an increased expression level of at least one marker selected from the group of CD 163, CD274 (PD-L1), CD21, CD33, CD32, CD36 and CD192 (CCR2),is indicative of an increased propensity of a severe respiratory condition; and/or (II) a reduced expression level of at least one marker selected from the group of CD49a, CD 172a, CD1 lc, CD 123, CD 169, CD86, HLA- DR, CD 123 / IL-3R and CD45RA is indicative of an increased propensity of a severe respiratory condition, preferably wherein for (I) and (II) increased or reduced expression level, respectively, is with respect to expression levels in cells in absence of a severe respiratory condition, preferably wherein for (I) and (II) increased propensity of severe respiratory conditions is with respect to the average propensity of a severe respiratory condition.

Within the present invention, it was surprisingly found that an increased frequency of certain cells or populations of cells expressing CD163, CD274 (PD-L1), CD326 (EpCAM), CD45RA, CD21, S100A9, CD206, CD209, CD 192 (CCR2), CD254, CD32, CD64, CD 172a, CD 11c, CD123, CD 169, CD86, HLA-DR, CDl lb, CD54, CD33, CD36, CD49a, CD31, gp38, CD80, CD34, CDla, CX3R1, CD195, CD68 and CD106 is/are indicative of an increased propensity severe respiratory conditions such as ARDS, admittance into ICU and/or development of cytokine release syndrome (CRS) and/or secondary hemophagocytic lymphohistiocytosis (sHLH).

Further, it was found that an increased frequency of certain cells or populations of cells expressing CD64, CD33, CD36, CD 169, S100A9, CD31, CD 192 (CCR2), CD209, CD254, CD32, CD 172a, HLA-DR, CD86, CD206, CD54, Ki67, Bcl6, CXCR5, and/or CD38 can be indicative of an increased propensity of patients infected with viral and/or bacterial pathogens of Severe respiratory conditions such ARDS, admittance into ICU and/or development of cytokine release syndrome (CRS) and/or secondary hemophagocytic lymphohistiocytosis (sHLH). Within the present invention, it was surprisingly found that an increased frequency of certain cells or populations of cells expressing CD64, CD33, CD36, CD169, S100A9, CD31, CCR2, CD209, CD254, CD32, CD 172a, HLA-DR, CD86, CD206, CD54, Ki67, Bcl6, CXCR5, and/or CD38 is indicative of an increased propensity of patients infected with viral and/or bacterial pathogens of Severe respiratory conditions such ARDS, admittance into ICU and/or development of cytokine release syndrome (CRS) and/or secondary hemophagocytic lymphohistiocytosis (sHLH).

The term “increased frequency” is understood as meaning an increase in the frequency of cells or populations of cells compared to commonly reported frequencies for the respective cells or populations of cells. For example, the frequency may be compared to the frequency of cells obtained from a healthy subject, wherein a healthy subject is a subject not infected by a viral and/or bacterial pathogen, not suffering from acute inhalation injury or not suffering from head, chest and/or other trauma at the time the frequency was determined.

Within the present invention, it was further surprisingly found that a reduced frequency of certain cells expressing CD49a is indicative of an increased propensity of patients infected with viral and/or bacterial pathogens of Severe respiratory conditions such as ARDS, admittance into ICU and/or development of cytokine release syndrome (CRS) and/or secondary hemophagocytic lymphohistiocytosis (sHLH).

Further, it was further surprisingly found that a reduced frequency of certain cells expressing CD49a, CD127 and/or CD27 can be indicative of an increased propensity of patients infected with viral and/or bacterial pathogens of Severe respiratory conditions such as ARDS, admittance into ICU and/or development of cytokine release syndrome (CRS) and/or secondary hemophagocytic lymphohistiocytosis (sHLH). The term "reduced frequency" is understood as meaning a reduction in the frequency of cells or populations of cells compared to commonly reported frequencies for the respective cells or populations of cells. For example, the frequency may be compared to the frequency of cells obtained from a healthy subject, wherein a healthy subject is a subject not infected by a viral and/or bacterial pathogen, not suffering from acute inhalation injury or not suffering from head, chest and/or other trauma at the time the frequency was determined. Within the invention, the term “increased propensity of severe respiratory conditions” is to be understood with respect to the average propensity of a severe respiratory condition. The skilled person is aware of the average propensity of a subject suffering from a severe respiratory condition. In this regard, the subject may be a healthy subject. However, it was surprisingly found that among subjects infected with a viral and/or bacterial pathogen, some have a higher propensity of a severe respiratory condition than the average of subjects infected with the same viral and/or bacterial pathogen. The panel of markers and the methods provided herein are indicative of a patient having an increased propensity of a severe respiratory condition compared with healthy subjects and/or the average subject infected with the same viral and/or bacterial pathogen.

Within the methods of the invention, it is preferred that the levels of expression of the molecular markers are determined using an antibody-based assay, in particular wherein the antibody- based assay is an antibody-based flow cytometry or mass cytometry assay. However, the skilled person is well-aware of methods suitable for determining expression levels of markers and which may thus be used within the invention.

In some embodiments, the invention relates to the method according to any one of the invention, wherein a classifier algorithm is used for quantifying the frequency of disease- associated immune cells in the plurality of human cells based on the unique state of the immune system.

It is preferred within the invention that a classifier algorithm is used to distinguish between cell populations indicative of Severe respiratory conditions and cell populations contraindicative of Severe respiratory conditions, in particular wherein a convolutional neural network is used.

In some embodiments, the invention relates to the method according to the invention, wherein the classifier algorithm comprises a vector algorithm, a convolutional neural network, a tree- based method, logistic regression

In some embodiments, the invention relates to the method according to the invention, wherein the classifier algorithm is used to distinguish between immune cell populations indicative of severe respiratory conditions and immune cell populations contraindicative of severe respiratory conditions.

In some embodiments, the invention relates to the method according to the invention, wherein the classifier algorithm is a selection algorithm. In some embodiments, the invention relates to the method according to the invention, wherein the classifier algorithm is a computer-implemented algorithm.

In a further embodiment, the invention relates to a computer-implemented method for determining the frequency of human immune cells in a plurality of cells, particularly circulating cells, the method comprising the steps of executing a classifier algorithm on a set of data comprising the levels of expression of two or more biomarkers selected from the group consisting of the biomarkers provided herein in a plurality of cells; determining the levels of expression of two or more biomarkers provided herein in a plurality of cells; and determining the frequency of human immune cells in the plurality of cells.

It is preferred that the classifier algorithm comprises one or of a combination of a support vector algorithm, a convolutional neural network, a tree-based method, logistic regression.

In a further embodiment, the invention relates to a computer program product containing instructions for performing the computer-implemented method provided herein.

Computer-readable program instructions for carrying out operations of the present invention may be assembler instructions, instruction-set-architecture (ISA) instructions, machine instructions, machine-dependent instructions, microcode, firmware instructions, state-setting data, or either source code or object code written in any combination of one or more programming languages, including an object-oriented programming language such as Smalltalk, C++ or the like, and conventional procedural programming languages, such as the “C” programming language or similar programming languages.

The computer-readable program instructions may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server.

In some embodiments, the invention relates to a method for predicting a clinical outcome in a patient infected with a pathogen, patients suffering from pancreatitis, a patient suffering from acute inhalation injury and/or a patient suffering from head, chest and/or other trauma, the method comprising the steps of: (a) defining a unique state of the immune system according to the method of the invention and/or quantifying the frequency of disease-associated immune cells according to the method the invention; (b) comparing the unique state of the immune system and/or the frequency of disease-associated immune cells of step (a) to a predictability reference pattern, wherein the predictability reference pattern is obtained from a reference population with a known clinical outcome; and c) determining the clinical outcome of the patient based on the comparison in step (b).

The term “clinical outcome”, as described herein, refers to development and/or progression of a condition, disease, disorder or symptoms thereof. As such, the term “clinical outcome” can embody for example a risk category, a likelihood value/category, severity value/category and/or a time period. In some embodiments, the clinical outcome described herein refers to development and/or progression of a severe respiratory condition. In some embodiments, the clinical outcome described herein refers to development and/or progression of ARDS.

The term “predictability reference pattern”, as used herein, refers to a pattern that allows distinguishing clinical outcomes from unique states of the immune systems and/or frequencies of disease-associated immune cells that is based on data of a reference population. In some embodiments, the predictability reference pattern comprises one or more thresholds to one or more parameters. In some embodiments, the predictability reference pattern described herein is obtained by a machine-learning technique. In some embodiments, the predictability reference pattern described herein is obtained by a classifier algorithm that comprises a convoluted neural network and/or logistic regression.

In a further embodiment, the invention relates to a method for predicting a severe clinical outcome in a patient infected with a viral and/or bacterial pathogen, the method comprising the steps of quantifying the frequency of human immune cells in a plurality of cells, particularly circulating cells, more particularly PBMCs, using the panel of markers provided herein or the methods provided herein; comparing the frequency of human immune cells obtained from a patient infected with a viral and/or bacterial pathogen, to the frequency of human immune cells, in a sample that has been obtained from a subject not infected with a viral and/or bacterial pathogen, not suffering from acute inhalation injury or not suffering from head, chest and/or other trauma; and determining a subject as having a predisposition for a severe clinical outcome, if the frequency of human immune cells obtained from a patient infected with a viral and/or bacterial pathogen, suffering from acute inhalation injury or suffering from head, chest and/or other trauma, is higher compared to the frequency of human immune cells in a sample from a subject not infected with a viral and/or bacterial pathogen, not suffering from acute inhalation injury or not suffering from head, chest and/or other trauma.

It is preferred that determining the subject as having a predisposition for a severe clinical outcome, comprises the use of a classifier algorithm, in particular wherein the classifier algorithm comprises a convoluted neural network and/or logistic regression.

Further, the invention relates to a panel of molecular markers as provided herein for use in stratifying patients infected with a viral and/or bacterial pathogen, patients suffering from acute inhalation injury or suffering from head, chest and/or other trauma into high- and low-risk groups at the time of hospitalization.

The means and methods provided by the invention can be used to predict the clinical outcome of subjects. This prediction can be used to assess the use of early interventions and to improve treatments.

Accordingly, the invention is at least in part based on the surprising finding, that the means and methods provided by the invention can be used to accurately predict the clinical outcome in patients at risk for severe respiratory conditions.

In some embodiments, the invention relates to a method for determining susceptibility of a patient towards a respiratory treatment composition comprising the steps of: a) defining a unique state of the immune system according to the method of the invention and/or quantifying the frequency of disease-associated immune cells according to the method of the invention; b) comparing the unique state of the immune system and/or the frequency of disease-associated immune cells of step (a) to a susceptibility reference pattern, wherein the susceptibility reference pattern is obtained from a reference population with known treatment outcome; and c) determining susceptibility of a patient towards a respiratory treatment composition based on the comparison of step (b).

The term “susceptibility reference pattern”, as used herein, refers to a pattern that allows distinguishing susceptibility to a respiratory treatment from unique states of the immune systems and/or frequencies of disease-associated immune cells that is based on data of a refence population. In some embodiments, the susceptibility reference pattern comprises one or more thresholds to one or more parameters. In some embodiments, the susceptibility reference pattern described herein is obtained by a machine-learning technique. In some embodiments, the susceptibility reference pattern described herein is obtained by a classifier algorithm that comprises a convoluted neural network and/or logistic regression.

In some embodiments, the respiratory treatment described herein is a treatment selected from the group of mechanical ventilation, airway pressure release ventilation, fluid management, steroids, nitric oxide and extracorporeal membrane oxygenation. In some embodiments, the respiratory treatment described herein is a treatment selected from the group of darunavir, oseltamivir, umifenovir, favipiravir, ribavirin, nafamostat mesylate, camostat mesylate, lopinavir, ritonavir, nelfmavir, teicoplanin, azithromycin, chloroquine, hydroxy chloroquine, thalidomide, bevacizumab, tocilizumab, sarilumab, anakinra, interferon (a, B, X), losartan, corticosteroid (e.g. methylprednisolone), ivermectin, nitazoxanide, emetine, famotidin, heparin, EIDD-2801 and dipyridamole.

Therefore, the means and method of the invention can estimate and/or predict which treatment is appropriate and likely to result in treatment success. As such, the means and methods of the invention can identify and/or classify (a) patient(s) in patients populations at risk to develop a severe respiratory condition that are likely to benefit from a certain respiratory treatment.

In some embodiments, the invention relates to the method for predicting a clinical outcome according to the invention or method for determining susceptibility according to the invention, wherein determining susceptibility of a patient towards a respiratory treatment composition or determining the clinical outcome of the patient comprises the use of a classifier algorithm, in particular wherein the classifier algorithm comprises a convoluted neural network and/or logistic regression.

Accordingly, the invention is at least in part based on the surprising finding, that the means and methods provided by the invention can be used to accurately predict the susceptibility to a respiratory treatment in patients at risk for severe respiratory conditions.

In some embodiments, the invention relates to the method for predicting a clinical outcome according to the invention or method for determining susceptibility according to the invention, wherein the reference population comprises at least one reference subject not having a severe respiratory condition.

The reference subject not having a severe respiratory condition (e.g. a healthy subject) can be used to define a norm value from which deviations indicate increased risk to develop a severe respiratory condition.

Accordingly, incorporation of a reference subject not having a severe respiratory condition can improve the predictive power/accuracy of the method described herein.

In some embodiments, the invention relates to the method for predicting a clinical outcome according to the invention or method for determining susceptibility according to the invention, wherein the reference population additionally comprises at least one reference subject that is a patient having a severe respiratory condition. The patient having a severe respiratory condition (e.g. a patient having ARDS) can be used as a reference value for the diseased state.

Accordingly, incorporation of a reference subject not having a severe respiratory condition and a patient having a severe respiratory condition can improve the predictive power/accuracy of the method described herein.

Various viral and/or bacterial pathogens can cause severe respiratory conditions. The panel of the invention and the methods of the invention can be used to determine propensity of a patient of developing a severe respiratory condition subsequent to an infection with any of these viral and/or bacterial pathogens. In some embodiments of the invention, the pathogens are SARS- CoV-2, Influenza virus A or B, Haemophilus, influenzae type b, respiratory syncytial virus, Pneumocystis jiroveci.

In some embodiments, the invention relates to a composition for use in the treatment of a severe respiratory condition in a) a patient infected with at least one pathogen, b) a patient suffering from an acute lung injury, c) patients suffering from pancreatitis, and/or d) a patient suffering from head, chest and/or other trauma, wherein the patient is determined as susceptible towards a treatment according to the method of the invention.

In some embodiments, the invention relates to an antiviral composition for use in the treatment of a severe respiratory condition in a patient infected with at least one virus, wherein the patient is determined as susceptible towards a treatment according to the method of any one of the invention.

In some embodiments, the invention relates to an antiviral composition for use in the treatment of a severe respiratory condition in a patient infected with at least one coronavirus, preferably SARS-CoV-2 wherein the patient is determined as susceptible towards a treatment according to the method of any one of the invention, preferably wherein the antiviral compound comprises a compound selected from the group of darunavir, oseltamivir, umifenovir, favipiravir, ribavirin, nafamostat mesylate, camostat mesylate, lopinavir, ritonavir, nelfmavir, teicoplanin, azithromycin, chloroquine, hydroxy chloroquine, thalidomide, bevacizumab, tocilizumab, sarilumab, anakinra, interferon (a, B, X), losartan, corticosteroid (e.g. methylprednisolone), ivermectin, nitazoxanide, emetine, famotidin, heparin, EIDD-2801 and dipyridamole.

The use and dosage of these compounds in viral diseases is known to the skilled person (see e.g., Tarighi, P., et ah, 2021, European Journal of Pharmacology, 173890 and documents and study protocols of trials cited therein).

The term “biomarker”, as used herein, refers to a molecule that is part of and/or generated by a cell and serves as an indicator for a disease/condition. Often a biomarker is a gene variant or a gene product, for example an RNA or a polypeptide. Within the present invention, the biomarkers are preferably proteins, preferably proteins that are localized on the surface of the cell, such that they are accessible to binding agents that can be used for the quantification of the biomarker. A biomarker may be any protein that is expressed on the surface of a cell. In certain embodiments, a biomarker is a cell surface protein that is known to be present on immune cells. However, within the present invention, a biomarker may be present in serum and/or plasma of a patient.

In certain embodiments, the expression levels of the biomarkers provided herein, or a subset thereof, may be measured in order to determine if a subject is likely to develop a certain medical condition, such as a severe respiratory condition, such as ARDS, admittance into ICU and/or development of cytokine release syndrome (CRS) and/or secondary hemophagocytic lymphohistiocytosis (sHLH). That is, in certain embodiments, the expression levels of all biomarkers provided herein may be determined in a cell in order to determine if said cell is indicative of a certain medical condition. In other embodiments, the expression levels of only a subset of the biomarkers provided herein may be determined in a cell in order to determine if said cell is indicative of a certain medical condition. In other embodiments, the expression levels of all biomarkers provided herein, or of a subset of the biomarkers provided herein may be determined in a cell together with the expression level of other biomarkers that are not provided herein in order to determine if said cell is indicative of a certain medical condition.

The terms “expression level” or “level of expression”, as used herein, refer to the absolute frequency/abundance of a biomarker described herein or the relative frequency/abundance as compared to a reference, in particular a known frequency/abundance on a healthy cell or a diseased cell to which the determined frequency/abundance can be compared. The expression level of a biomarker in a single cell may be measured by any method known in the art. The expression level of a biomarker may be measured on the nucleic acid level or on the protein level. That is, in certain embodiments, the expression level of a biomarker in a cell may be measured by determining the levels of mRNAs in said cell by methods known in the art, such as sequencing and/or PCR-based methods. In other embodiments, the expression level of a biomarker may be measured by determining the level of a protein in or on the surface of said cell. Measuring the level of a protein may comprise the use of a binding agent, such as an antibody that specifically binds to a target protein. The skilled person is aware of methods to determine the expression level of a protein in a single cell. Preferably, flow cytometry or mass cytometry methods known in the art may be used for determining the expression level of a protein in a single cell. Within the present invention, it is preferred that the binding agent binds to proteins on the cell surface. However, the present invention also encompasses embodiments wherein the biomarkers are intracellular biomarkers or biomarkers in the serum/plasma of a patient. In this case, cells may be fixated and/or permeabilized before addition of the binding agent.

The term “flow cytometry”, as used herein, refers to methods for the analysis of cells or particulate samples well known to the skilled artisan, such as those provided by Becton- Dickinson, Cytomation, Partec, Luminex, or Beckman-Coulter. Flow cytometry can encompass multiparametric DNA analysis, platelet studies, reticulocyte enumeration, cell biology/functional studies, innovative research in immunobiology, cell physiology, molecular biology, genetics, microbiology, water quality and plant cell analysis as well as a broad range of research applications. Current flow cytometers are manufactured with the ability to measure more than one, preferably four or more separate detectable labels simultaneously. Using methods for flow cytometric analysis, a specifically labeled molecule, such as an antibody, is added to the cellular or particulate sample believed to contain an analyte of interest. The antibody is labeled with an appropriate detectable label, such as a fluorophore, which permits detection of those cells or particles comprising the analyte of interest at a detectable level. The analysis can involve quantification and/or detection of the analyte, and may also involve sorting or harvesting the cells or particles possessing the analyte of interest.

Mass cytometry is a mass spectrometry technique based on inductively coupled plasma mass spectrometry and time of flight mass spectrometry used for the determination of the properties of cells (cytometry). In this approach, antibodies are conjugated with isotopically pure elements, and these antibodies are used to label cellular proteins. Cells are nebulized and sent through an argon plasma, which ionizes the metal-conjugated antibodies. The metal signals are then analyzed by a time-of-flight mass spectrometer. The approach overcomes limitations of spectral overlap in flow cytometry by utilizing discrete isotopes as a reporter system instead of traditional fluorophores which have broad emission spectra. The number of biomarkers that are analyzed in a cell may be dependent on the experimental method used. That is, the number of biomarkers may be dependent on the number of labels that can be simultaneously detected by a flow cytometer or on the number of metal-conjugated antibodies that are available for mass cytometry applications. By measuring the expression of biomarkers in each cell in a plurality of cells, it is possible to determine the frequency of cells that are indicative of a certain medical condition. The “relative frequency” of an event is defined as the number of times that the event occurs during experimental trials, divided by the total number of trials conducted. That is, the relative frequency of cells that are indicative of a certain medical condition, such as a severe respiratory condition, in a plurality of cells may be determined by dividing the number of cells that are indicative of a certain medical condition by the total number of cells in a plurality of cells. A “plurality of cells”, as used herein, is defined as two or more than two cells.

Some biomarkers of the biomarkers provided herein may be more relevant for diagnosing a certain medical condition, such as a severe respiratory condition. Accordingly, a weight (or “filter weight”) may be placed on the biomarkers in the set of biomarkers that reflects the relevance of each biomarker for a certain medical condition. That is, a biomarker that is more relevant for diagnosing a certain medical condition may be weighted more heavily than a biomarker that is less relevant for diagnosing the same medical condition. A biomarker with a higher weight or filter weight may then contribute more to the decision if a cell is indicative of developing a severe respiratory condition than a biomarker with a lower weight. If a biomarker is relevant for diagnosing a certain medical condition may be determined by analyzing the expression of said biomarker in subjects that suffer from said medical condition and/or subjects that do not suffer from said medical condition.

Comparing the expression level of biomarkers between cells and determining the relevance of each biomarker may be performed manually. However, it is preferred in the present invention that these steps are performed with the help of a classifier algorithm.

Within the present invention, a classifier algorithm may be used for distinguishing cell types, in particular cells indicative of a high propensity of a severe respiratory condition vs. cells contraindicative of a severe respiratory condition. To be able to distinguish between these cell types, the classifier algorithm may be pre-trained with a training data set. That is, in certain embodiments, the classifier algorithm may be pre-trained with data sets that have been obtained with samples from known subjects. For example, the classifier algorithm may be pre-trained with data sets that have been generated with samples that have been obtained from subjects that are known to have a high propensity of developing a severe respiratory condition and with data sets that have been generated with samples that have been obtained from subjects that are known not to have a high propensity of developing a severe respiratory condition. Alternatively, when distinguishing such cells among subjects, the classifier algorithm may be pre-trained with data sets that have been generated with samples that have been obtained from subjects that are known to have a severe respiratory condition and with data sets that have been generated with samples that have been obtained from subjects that are known not to have a severe respiratory condition. The training data set that is used for pre-training the classifier algorithm may comprise any number of data sets that have been generated with samples from individual subjects. Preferably, the training data set comprises at least 5, at least 6, at least 7, at least 8, at least 9, at least 10, at least 11, at least 12, at least 13, at least 14 or at least 15 data sets that have been generated with samples from individual subjects for each condition that is to be distinguished by the classifier algorithm.

In another embodiment, the invention relates to the method according to the invention, wherein a convolutional neural network is used to distinguish between a cell or cells indicative of developing a severe respiratory condition and a cell or cells contraindicative of developing a severe respiratory condition, respectively.

Within the present invention, a convolutional neural network may be used for distinguishing a cell or cells indicative of developing a severe respiratory condition and a cell or cells contraindicative of developing a severe respiratory condition, respectively. However, it is preferred within the present invention, that the convolutional neural network CellCnn is used for distinguishing a cell or cells indicative of developing a severe respiratory condition and a cell or cells contraindicative of developing a severe respiratory condition, respectively. The CellCnn convolutional neural network has been described previously (Bodenmiller et ah, Nat Biotechnol, 2012, 30(9), 858-867; Amir et ah, Nat Biotechnol, 2013, 31(5), 545-552; Levine et ah, Cell, 2015, 162(1), 184-197; Horowitz et al., Sci Transl Med, 2013, 5(208), 208ral45) and is publicly available (https://github.com/eiriniar/CellCnn).

It is preferred that the cells comprised in the sample are human cells. More preferably, the cells that are comprised in the sample have been obtained from the blood of a subject. Even more preferably, the cells that are comprised in the plurality of cells are circulating cells, preferably peripheral blood mononuclear cells (PBMCs). The skilled person is aware of methods to obtain PBMCs from a subject. The subject may be any human subject, for example a healthy subject known to be infected with a viral and/or bacterial pathogen or a subject not previously known to be infected. The term “PBMC” as used herein refers to peripheral blood mononuclear cells isolated from human peripheral blood preparations e.g. by use of a density gradient (e.g. Ficoll, PanColl). “PBMC” consists of lymphocytes and monocytes.

In another embodiment, the invention relates to the method according to the invention, wherein the levels of the biomarkers are determined using an antibody-based assay. As mentioned above, it is preferred that the expression levels of the biomarkers are determined in an antibody- based assay. That is, any assay that comprises the use of antibodies and is suitable for determining the expression level of a biomarker may be used in the present invention. Preferably, antibodies are used that bind directly to the biomarker.

The term "antibody" as used herein includes whole antibodies and any antigen binding fragments (i.e., "antigen-binding portions") or single chains thereof. It also includes all other types of antibody-like molecules such as diabodies, triabodies, nanobodies and the like. An "antibody" refers to a glycoprotein comprising at least two heavy (H) chains and two light (L) chains inter-connected by disulfide bonds, or an antigen binding portion thereof. Each heavy chain is comprised of a heavy chain variable region (abbreviated herein as VH) and a heavy chain constant region. In certain naturally occurring antibodies, the heavy chain constant region is comprised of three domains, CHI, CH2 and CH3. In certain naturally occurring antibodies, each light chain is comprised of a light chain variable region (abbreviated herein as VL) and a light chain constant region. The light chain constant region is comprised of one domain, CL. The VH and VL regions can be further subdivided into regions of hypervariability, termed complementarity determining regions (CDR), interspersed with regions that are more conserved, termed framework regions (FR). Each VH and VL is composed of three CDRs and four FRs, arranged from amino-terminus to carboxy -terminus in the following order: FR1, CDR1, FR2, CDR2, FR3, CDR3, FR4. The variable regions of the heavy and light chains contain a binding domain that interacts with an antigen.

The term antibody as used herein comprises IgGl, IgG2, IgG3, IgG4, IgM, IgAl, IgA2, IgD, and IgE antibodies. Antibodies typically bind specifically to their cognate antigen with high affinity, reflected by a dissociation constant (KD) of 10⁵ to 10 ¹¹ M or less. Any KD greater than about 10⁴M is generally considered to indicate nonspecific binding. The phrase "antigen binding portion" of an antibody, as used herein, refers to one or more fragments of an antibody that retain the ability to specifically bind to an antigen (e.g., a biomarker of the present invention). It has been shown that the antigen-binding function of an antibody can be performed by fragments of a full-length antibody. Examples of binding fragments encompassed within the term "antigen-binding portion" of an antibody include (i) a Fab fragment, a monovalent fragment consisting of the VL, V H, CL and CHI domains; (ii) a F(ab')2 fragment, a bivalent fragment comprising two Fab fragments linked by a disulfide bridge at the hinge region; (iii) a Fd fragment consisting of the VH and CHI domains; (iv) a Fv fragment consisting of the VL and VH domains of a single arm of an antibody, (v) a dAb fragment (Ward et ak, (1989) Nature 341:544-546), which consists of a VH domain; and (vi) an isolated complementarity determining region (CDR) or (vii) a combination of two or more isolated CDRs which may optionally be joined by a synthetic linker. Furthermore, although the two domains of the Fv fragment, VL and VH, are coded for by separate genes, they can be joined, using recombinant methods, by a synthetic linker that enables them to be made as a single protein chain in which the VL and VH regions pair to form monovalent molecules (known as single chain Fv (scFv); see e.g., Bird et al. (1988) Science 242:423-426; and Huston et al. (1988) Proc. Natl. Acad. Sci. USA 85:5879-5883). Such single chain antibodies are also intended to be encompassed within the term "antigen-binding portion" of an antibody. These antibody fragments are obtained using conventional techniques known to those with skill in the art, and the fragments are screened for utility in the same manner as are intact antibodies. Antigen-binding portions can be produced by recombinant DNA techniques, or by enzymatic or chemical cleavage of intact immunoglobulins. Within the present invention, the antibodies are preferably labeled to facilitate detection and/or quantification of a biomarker. For example, antibodies may be labeled with a fluorophore to allow detection and/or quantification of biomarkers in flow cytometry-based assays or metal isotopes to allow detection and/or quantification of biomarkers in mass cytometry-based assays. Accordingly, in another embodiment, the invention relates to the method according to the invention, wherein the antibody-based assay is an antibody-based flow cytometry or mass cytometry assay.

The invention also relates to a kit comprising two or more agents suitable for detecting two or more markers of the panel provided herein. Reference throughout this specification to "one embodiment", "an embodiment", "a particular embodiment", "a related embodiment", "a certain embodiment", "an additional embodiment", “some embodiments”, “a specific embodiment” or "a further embodiment" or combinations thereof means that a particular feature, structure or characteristic described in connection with the embodiment is included in at least one embodiment of the present invention. Thus, the appearances of the foregoing phrases in various places throughout this specification are not necessarily all referring to the same embodiment. Furthermore, the particular features, structures, or characteristics may be combined in any suitable manner in one or more embodiments. It is also understood that the positive recitation of a feature in one embodiment, serves as a basis for excluding the feature in a particular embodiment.

Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. Although methods and materials similar or equivalent to those described herein can be used in the practice or testing of the present invention, suitable methods and materials are described below. In case of conflict, the present specification, including definitions, will control. In addition, the materials, methods, and examples are illustrative only and not intended to be limiting.

The general methods and techniques described herein may be performed according to conventional methods well known in the art and as described in various general and more specific references that are cited and discussed throughout the present specification unless otherwise indicated. See, e.g., Sambrook et al, Molecular Cloning: A Laboratory Manual, 2d ed., Cold Spring Harbor Laboratory Press, Cold Spring Harbor, N.Y. (1989) and Ausubel et ah, Current Protocols in Molecular Biology, Greene Publishing Associates (1992), and Harlow and Lane Antibodies: A Laboratory Manual, Cold Spring Harbor Laboratory Press, Cold Spring Harbor, N.Y. (1990).

While aspects of the invention are illustrated and described in detail in the figures and foregoing description, such illustration and description are to be considered illustrative or exemplary and not restrictive. It will be understood that changes and modifications may be made by those of ordinary skill within the scope and spirit of the following claims. In particular, the present invention covers further embodiments with any combination of features from different embodiments described above and below.

Furthermore, in the claims the word "comprising" does not exclude other elements or steps, and the indefinite article "a" or "an" does not exclude a plurality. A single unit may fulfill the functions of several features recited in the claims. The terms “essentially”, “about”, “approximately” and the like in connection with an attribute or a value particularly also define exactly the attribute or exactly the value, respectively. Any reference signs in the claims should not be construed as limiting the scope

Brief Description of Figures

FIG. 1 : Boxplots comparing the level of expression in pg/mL of the soluble markers which were found to have an increased level of expression in patients developing ARDS versus patients not developing ARDS. P-values determined by Wilcoxon rank-sum test.

FIG. 2: Boxplots comparing the relative frequency of ARDS-associated cells selected by CellCnn analysis in PBMC samples from patients developing ARDS versus patients not developing ARDS. P-value = 3.8e-7, Wilcoxon rank-sum test.

FIG. 3: t-SNE map of cells from all samples in the discovery cohort (5000 cells randomly sub-sampled), showing in black ARDS-associated cells selected by CellCnn analysis. ARDS- associated cells can be found in multiple clusters on the t-SNE map, corresponding to different biological cell types.

FIG. 4: Smoothed densities of normalized, arcsinh-transformed marker expression for the top 9 most informative markers from a panel of markers specific to the myeloid cell lineage in ARDS-associated selected cells (solid line) vs. all measured cells (dotted line). K-S indicates the Kolmogorov-Smirnov test statistic for each comparison. Markers are shown in decreasing order of separation between the selected cells’ distribution and the background distribution.

FIG. 5 : Smoothed densities of normalized, arcsinh-transformed marker expression for the top 9 most informative markers from a panel of markers specific to the lymphoid cell lineage in ARDS-associated selected cells (solid line) vs. all measured cells (dotted line). K-S indicates the Kolmogorov-Smirnov test statistic for each comparison. Markers are shown in decreasing order of separation between the selected cells’ distribution and the background distribution.

Figure 6: Confusion matrices based on predictions of one training CellCnn models.

Figure 7: Confusion matrices based on predictions of two validation CellCnn models, either without (A) or with (B) addition of CRP and PCT.

Examples Example 1

Clinical Study design

Phase I Discovery: An open label study for the discovery of biomarkers for the prediction of severe susceptibility to SARS-CoV-2 (Covid-19) clinical deterioration.

Study Population

Samples were isolated from 37 patients (9 COVID-19^negARDS^pos, 13 COVID-19^posARDS^pos, and 15 COVID-19^posARDS^neg patients) with severe symptomatology that required hospitalization, consisting of peripheral blood mononuclear cells (PBMCs). For the same 37 patient cohort, a battery of tests were performed including: basic clinical parameters, luminex based plasma protein quantification, conventional flow cytometry profiling of basic immune types and amino acid profiling, all described in “Data Types” section.

Clinical Investigation Objectives

Primary: The objective of our initial discovery study was to identify highly sensitive single-cell biomarkers alone or combined with clinical data predicting ARDS development in COVID- 19pos or COVID-19neg or pneumonia or sepsis patients at time of hospitalization.

Secondary: The widespread infection of Covid-19 has placed an unprecedented strain on the healthcare infrastructures of all affected countries. Approximately 5% of Covid-19 patients will require admission into intensive care units (ICUs) and eventually assisted mechanical ventilation. This study will aim to identify predictive markers that can pinpoint, at an early stage, the need for and total time of treatment in an ICU.

High concentrations of cytokines (termed cytokine storms) have been recorded in the plasma of critically ill patients infected with Covid-19. The high mortality associated with Covid-19 might be due to virally driven hyperinflammation. A hallmark of cytokine release is elevated serum interleukin-6 (IL-6) which correlates with respiratory failure, ARDS and adverse outcomes. A key goal of this study will be to identify cell and/or genetic markers in newly hospitalized patients predictive of such hyperinflammatory reactions or ARDS development. *timepoints of sampling (DO - D7)

Stakeholders & Roles

Clinical Investigational Site: Rennes University Hospital (CHU). Patient recruitment, sample & data acquisition

Clinical data analysis: Scailyte AG. Biomarker discovery and validation.

Data Types

Clinical Parameters: Full, anonymized clinical data e.g. Age, gender, BMI, pre-existing condition, ICU admission, etc.

CyTOF panels: CyTOF measurements using two panels suitable two characterize lymphoid and myeloid lineage within PBMC

Luminex profiling: Panel of analytes including cytokines (e.g. IL6)

Amino acid profiling

Flow cytometry: Total Lymphocyte #, B-Cell count, T-Cell count, CD4/CD8 ratio, NK cell count, analytes and markers Sample Size

37 patients for which liquid biopsies (patient blood) were be analyzed at 2 time points (where possible) DO and D7

Inclusion Criteria

Patients may be enrolled at the Day of Screening / Enrolment if they meet all the following criteria:

Able and willing to provide voluntary written Informed Consent and sign the ICF to participate in the study prior to any study related procedure Age > 18 years old

Men and women with confirmed positive test for Covid-19 according to WHO laboratory guidelines

Men and women with severe respiratory symptoms

Patients whose symptomatology supersedes self-management guidelines according to local/regional public health protocols for home isolation and require hospitalization.

The planned study has no influence on further therapeutic steps.

Exclusion Criteria

Patients meeting any of the following criteria at Day of Screening / Enrolment will be excluded from entry into the study:

Patient is unlikely to cooperate or is legally incompetent, including patients who are institutionalized by court or official order

Any condition which could interfere with the patient’s ability to comply with the study, Cancer patients undergoing chemo, immunotherapy

Patients who are immunocompromised or who have existing chronic viral infections (HIV, HBA/B/C, HTLV, etc)

Methodology

The biomarkers will be identified through analysis of a patient cohort. PBMC’s from blood of 37 patients, will be analyzed using mass cytometry (cytometry by time of flight, CyTOF) and two predefined antibody panels that will allow a comprehensive characterization of the patient’s immune system

Patient data Medical data collected will be curated into a format for integration into our internal deep learning platform ScaiVision™ or another suitable data analysis workflow that uses patient data as a tool to identify disease-related molecular profiles/or cell identity biomarkers.

Data analytics

Data analysis will be performed following the below workflow Data pre-processing including bead normalization and demultiplexing Quality control to check for technical or batch effects Automated cell-type annotation

Supervised discovery of predictive biomarkers using a convolutional neural network

The goal is to validate at least one cell-identity biomarker profile that is predictive of one or more of the stated endpoints with a sensitivity and specificity >= 80%

Project Duration

6 months

Statistical Analysis

We will identify signatures of biomarkers to predict susceptibility to clinical deterioration using a machine learning algorithm based on CellCnn (Arvaniti and Claasen 2017). To create an independent validation set, we randomly splitted off 30% of the 37 samples before starting network training. We considered any biomarker profile passing the accuracy significance threshold (of >80%) as a potential candidate. 5-fold cross-validation is used to evaluate the reproducibility of the CellCnn algorithm on the specific dataset.

GCP Statement

This study was conducted in compliance with the protocol, the current version of the Declaration of Helsinki, the ICH-GCP or ISO EN 14155 (as far as applicable) as well as all national legal and regulatory requirements.

Explanation of work done

Biomarker discovery was carried out on a cohort of PBMC samples collected from 37 patients at the time of hospitalization, 28 of which were confirmed as being positive for COVID-19 via PCR, and 9 of which were COVID-19 negative. CyTOF data was acquired from these samples using two different antibody panels: panel 1 consisted of 37 antibodies mainly targeting immune cells from the myeloid lineage, and panel 2 consisted of 36 antibodies mainly targeting immune cells from the lymphoid lineage. The data was pre-processed by applying bead normalization to the entire dataset (Finck, R. et al. Cytometry A 83 A, 483-494 (2013)) and then applying an arcsinh transformation with a cofactor of 5 (Nowicka, M. et al. FlOOOResearch 6, 748 (2019)). Finally, data from all samples were concatenated, and each measured parameter was standardized by subtracting the mean and dividing by the standard deviation of that parameter across all cells (Z-score transformation).

Patient samples were divided into two groups, consisting of those patients that experienced clinically-defined ARDS (both COVID-positive and COVID-negative), and those that did not experience ARDS during hospitalization. This resulted in 22 ARDS samples and 15 non-ARDS samples. Approximately 30% of samples from each group (7 ARDS and 5 non-ARDS) were set aside to use for model validation. The remaining 25 samples were used to train a series of CellCnn neural networks to distinguish between the ARDS and non-ARDS groups. 50 such networks were trained with randomly chosen hyperparameters. The mean accuracy achieved for predicting the group of the validation samples was 79%, while the highest-performing network achieved an accuracy of 93% and was selected for further analysis.

CellCnn learned three filters for the best-performing network, of which one was positively correlated with ARDS and one was positively correlated with non-ARDS status. Using the weights from the filter positively correlated with ARDS, the inventors calculated a filter response score for every cell in the dataset. These scores can be used to set a threshold to determine the cells predictive of ARDS. A range of thresholds were tested, and the threshold that best separated ARDS and non-ARDS patient samples by relative frequency of ARDS- associated cells was chosen as 13.

Validation

An independent validation cohort of 17 patients was recruited, consisting of 5 COVID- 19^posARDS^pos, 3 CO VID- 19^pos ARD S^neg, and 9 COVID-19^negARDS patients. PBMC samples were obtained from the patients in the validation cohort and were analyzed using the same CyTOF panels as for the discovery cohort. The same pre-processing steps for the data were applied. The ARDS status of all samples in the validation cohort was predicted using the best performing CellCnn network trained on the discovery cohort. Using CyTOF data alone, this achieved 88% accuracy with an AUC of 0.95. By integrating clinical data into the CellCnn network, an accuracy of 94% with an AUC of 0.95 was achieved.

Table 1 - Biomarkers indicative of a severe respiratory condition

Table 2 - Biomarkers enriched or depleted in severe respiratory conditions

Table 3 - Soluble markers

Table 4 - cell lineage markers

Example 2

CellCnn was trained on a CyTOF dataset of 37 patient samples using a panel of 37 markers specific to the myeloid and cell lineage, and the endpoints(ARDS or non-ARDS) (Table 5 Fig. 6). The network was trained with or without the addition of clinical data parameters and the analyte panel (Table 3), using different sets of hyperparameters. Hyperparameters that set the training run of the network have been adjusted to tune the specificity and sensitivity of the prediction.

Table 5 - Myeloid marker

Example 3

The network with the highest validation accuracy was used to run on an independent CyTOF dataset of 17 patient samples using the same markers specific to the myeloid and cell lineage to confirm generalisability (Table 5, Fig. 7A). The results were validated using the same parameters of Example 2 to validate the results.

Example 4

The network with the highest validation accuracy was used to run on an independent CyTOF dataset of 17 patient samples using the same markers specific to the myeloid and cell lineage, and the soluble markers (Table 5) to confirm generalisability (Fig. 7B). The network was validated using the same parameters of Example 2 to validate the results.

Example 5

New Clinical Study design

Phase II Discovery: An open label study for the discovery of biomarkers for the prediction of severe respiratory syndrome onset.

Study Population

Samples are going to be isolated from >100 patients with severe symptomatology that required hospitalization, consisting of peripheral blood mononuclear cells (PBMCs). For the same >100 patient cohort, a battery of tests is going to be performed including: mass cytometry PBMC analysis, clinical parameters collection, luminex based plasma protein quantification, all described in “Data Types” section below.

Clinical Investigation Objectives

Primary:

The objective of our discovery study is to identify highly sensitive single-cell biomarkers alone or combined with clinical data predicting ARDS development in pneumonia or sepsis patients at time of hospitalization.

Secondary: The widespread infection of COVID-19 has placed an unprecedented strain on the healthcare infrastructures of all affected countries. However, while COVID-19 is being contained with the recent vaccines, pneumonia and sepsis patients keep on developing the same syndrome unnoticed. Approximately 10% of pneumonia and 30% of sepsis patients will require admission into intensive care units (ICUs) and eventually assisted mechanical ventilation. This study will aim to identify predictive markers that can pinpoint, at an early stage, the need for and total time of treatment in an ICU.

ARDS is known to be arising from heterogenous backgrounds (i.e. Trauma and pancreatitis among others) and to result in a high mortality driven by hyperinflammation in the lungs. Our efforts are going to investigate whether several phenotypes of the disease which correlates with respiratory failure, ARDS and adverse outcomes, can be also pinpoint with this methodology. A key goal of this study will be to identify cell and/or genetic markers in newly hospitalized patients predictive of such hyperinflammatory reactions or ARDS development.

Stakeholders & Roles

Clinical data analysis: Scailyte AG. Biomarker discovery and validation.

Data Types

Clinical Parameters: Full, anonymized clinical data e.g. Age, gender, BMI, pre-existing condition, ICU admission, etc. CyTOF panels: CyTOF measurements using one or two panels suitable two characterize lymphoid and myeloid lineage within PBMC Luminex profiling: Panel of analytes including cytokine

Sample Size

>100 patients for which liquid biopsies (patient blood) are going to be analyzed at 2-3 time points (where possible) DO and D5 and D7

Inclusion Criteria

Men and women hospitalized with pneumonia according to the ATS guidelines (Metlay J.P. et al., American Thoracic Society, 2019)

Men and women hospitalized with pneumonia and with medium-severe respiratory symptoms Men and women hospitalized with sepsis according to the Sepsis-3 guidelines (Singer M. et al., JAMA, 2016)

The planned study has no influence on further therapeutic steps.

Exclusion Criteria

Patients that suffer of severe symptomatology and/or ARDs

Methodology The biomarkers are going to be identified through analysis of a patient cohort. PBMC’s from blood of >100 patients, will be analyzed using mass cytometry (cytometry by time of flight, CyTOF) and one or two predefined antibody panels that will allow a comprehensive characterization of the patient’ s immune system

Patient data

Medical data collected will be curated into a format for integration into our internal deep learning platform ScaiVision™ or another suitable data analysis workflow that uses patient data as a tool to identify disease-related molecular profiles/or cell identity biomarkers.

Data analytics

Data analysis will be performed following the below workflow:

Data pre-processing including bead normalization and demultiplexing Quality control to check for technical or batch effects Automated cell-type annotation

Project Duration 12 months

Statistical Analysis

We will identify signatures of biomarkers to predict susceptibility to clinical deterioration using a machine learning algorithm based on CellCnn (Arvaniti and Claasen, Nat. Comm, 2017). To create an independent validation set, we randomly splitted off 50% of the >100 samples before starting network training. We considered any biomarker profile passing the accuracy significance threshold (of >80%) as a potential candidate. 5-fold cross-validation is used to evaluate the reproducibility of the CellCnn algorithm on the specific dataset.

Claims

1. A panel of markers, which in combination of at least two markers, define a unique state of the immune system in patients infected with a pathogen, patients suffering from pancreatitis, patients suffering from acute lung injury and/or patients suffering from head, chest and/or other trauma, wherein the markers are predictive of a severe respiratory condition.

2. The panel of markers of claim 1, comprising at least 3, 4, 5, 6, 7, 8, 9, 10 molecular markers.

3. The panel of markers of claim 1 or 2, wherein the at least two molecular markers are selected from the group consisting of CD163, CD274 (PD-L1), CD45RA, CD21, CD326 (EpCAM), S100A9, CD206, CD209, CD 192 (CCR2), CD254, CD32, CD64, CD 172a, CD 123, CD 169, CD86, HLA-DR, CD54, CD33, CD36, CD49a, CD31, gp38, CD80, CD34, CD la, CX3R1, CD 195, CD68, and CD 106 .

4. The panel of markers of claim 3 further comprising one or more molecular markers selected from the group consisting of PCT, CRP, G-CSF, GM-CSF, CXCL1, IFNg, IL- lra, IL-lb, CXCL8, IL-10, IL12p70, IL-18, IL-6, CXCL10, CCL2, CCL22, CXCL9, TNFa and VEGFa.

5. The panel of markers according to claim 3 or 4, wherein the panel additionally comprises one or more cell lineage molecular marker(s), in particular one or more marker(s) selected from the group consisting of CD45, CD3, CD14, CD16, CD19, CD 11c, CD lib, and CD20.

6. The panel of markers according to any one of claims 1 to 5 additionally comprising at least one non-molecular marker.

7. The panel of markers according to claim 6, wherein the non-molecular marker is at least one marker selected from the group of age, BMI, body temperature, systolic blood pressure, diastolic blood pressure, mean arterial blood pressure, heart rate and respiratory rate.

8. A method for defining a unique state of an immune system, the method comprising the steps of: a) determining the level(s) of expression and/or level(s) of protein of the molecular markers of the panel of markers according to claim 1 to 5 in at least one cell, plasma and/or serum; and b) defining a unique state of the immune system based on the levels of expression and/or level(s) of protein of step (a).

9. The method according to claims 8, wherein the levels of expression of the molecular markers are determined using an antibody-based assay, in particular wherein the antibody-based assay is an antibody-based flow cytometry or mass cytometry assay.

10. The method according of claim 8 or 9, wherein step (a) additionally comprises retrieving the non-molecular markers of the panel of markers according to claim 6 or 7 and wherein defining a unique state of the immune system in step (b) is additionally based on the retrieved non-molecular markers of step (a).

11. A method for quantifying the frequency of disease-associated immune cells in a plurality of human cells, the method comprising the steps of: i) defining a unique state of an immune system in a plurality of human cells according to the method according to claim 8 to 10; and ii) quantifying the frequency of disease-associated immune cells in the plurality of human cells based on the unique state of the immune system.

12. The method according to claim 11, wherein the human cells are immune cells from the myeloid lineage.

13. The method according to claim 12, wherein

(I) an increased expression level of at least one marker selected from the group of CD163, CD274 (PD-L1), CD21, CD33, CD32, CD36 and CD192 (CCR2),

CD326 (EpCAM),is indicative of an increased propensity of a severe respiratory condition; and/or (II) a reduced expression level of at least one marker selected from the group of CD49a, CD 172a, CDllc, CD123, CD169, CD86, HLA-DR, CD123 / IL-3R and CD45RA is indicative of an increased propensity of a severe respiratory condition, preferably wherein for (I) and (II) increased or reduced expression level, respectively, is with respect to expression levels in cells in absence of a severe respiratory condition, preferably wherein for (I) and (II) increased propensity of severe respiratory conditions is with respect to the average propensity of a severe respiratory condition.

14. The method according to any one of claims 11 to 13, wherein a classifier algorithm is used for quantifying the frequency of disease-associated immune cells in the plurality of human cells based on the unique state of the immune system.

15. The method according to claim 14, wherein the classifier algorithm comprises a vector algorithm, a convolutional neural network, a tree-based method, logistic regression

16. The method according to claims 14 or 15, wherein the classifier algorithm is used to distinguish between immune cell populations indicative of severe respiratory conditions and immune cell populations contraindicative of severe respiratory conditions.

17. The method according to any one of claims 14 to 16, wherein the classifier algorithm is a selection algorithm.

18. The method according to any one of claims 14 to 17, wherein the classifier algorithm is a computer-implemented algorithm.

19. A computer program product containing instructions for performing the computer- implemented method according to claim 18.

20. A method for predicting a clinical outcome in a patient infected with a pathogen, a patient suffering from acute inhalation injury or a patient suffering from head, chest and/or other trauma, the method comprising the steps of:

(a) defining a unique state of the immune system according to the method of any one of claims 8 to 10 and/or quantifying the frequency of disease-associated immune cells according to the method of any one of claims 11 to 18; (b) comparing the unique state of the immune system and/or the frequency of disease-associated immune cells of step (a) to a predictability reference pattern, wherein the predictability reference pattern is obtained from a reference population with a known clinical outcome; and c) determining the clinical outcome of the patient based on the comparison in step (b).

21. A method for determining susceptibility of a patient towards a respiratory treatment composition comprising the steps of: a) defining a unique state of the immune system according to the method of any one of claims 8 to 10 and/or quantifying the frequency of disease-associated immune cells according to the method of any one of claims 11 to 18; b) comparing the unique state of the immune system and/or the frequency of disease-associated immune cells of step (a) to a susceptibility reference pattern, wherein the susceptibility reference pattern is obtained from a reference population with known treatment outcome; and c) determining susceptibility of a patient towards a respiratory treatment composition based on the comparison of step (b).

22. The method for predicting a clinical outcome according to claim 20 or method for determining susceptibility according to claim 21, wherein determining susceptibility of a patient towards a respiratory treatment composition or determining the clinical outcome of the patient comprises the use of a classifier algorithm, in particular wherein the classifier algorithm comprises a convoluted neural network and/or logistic regression.

23. The method for predicting a clinical outcome according to claim 20 or 22 or method for determining susceptibility according to claim 21 or 22, wherein the reference population comprises at least one reference subject not having a severe respiratory condition.

24. The method for predicting a clinical outcome according to any one of the claims 20, 22 or 23 or method for determining susceptibility according to any one of the claims 21 to 23, wherein the reference population additionally comprises at least one reference subject that is a patient having a severe respiratory condition.

25. A composition for use in the treatment of a severe respiratory condition in a) a patient infected with at least one pathogen, b) a patient suffering from an acute lung injury, c) patients suffering from pancreatitis, and/or d) a patient suffering from head, chest and/or other trauma, wherein the patient is determined as susceptible towards a treatment according to the method of any one of the claims 21 to 24.

26. The panel of markers according to any one of claims 1 to 7, the method for defining a unique state of an immune system according to any one of claims 8 to 10, the method for quantifying the frequency of disease-associated immune cells according to any one of claims 11 to 18, the computer program product according to claim 19, the method for predicting a clinical outcome according to any one of the claims 20, 22 to 24, the composition for use according to claim 25 or method for determining susceptibility according to any one of the claims 21 to 24, wherein the severe respiratory condition comprises at least one condition selected from the group of acute respiratory distress syndrome (ARDS) status, admittance into ICU, requirement of ventilatory management, development of cytokine release syndrome (CRS) and secondary hemophagocytic lymphohistiocytosis (sHLH).

27. The panel of markers according to claim 26, the method for defining a unique state of an immune system according to claim 26, the method for quantifying the frequency of disease-associated immune cells according to claim 26, the computer program product according to claim 25, the method for predicting a clinical outcome according to claim 26, the composition for use according to claim 26 or method for determining susceptibility according to claim 26, wherein the severe respiratory condition is ARDS, admittance into ICU and/or requirement of ventilatory management.

28. The panel of markers according to any one of claims 1 to 7, 26 or 27, the method for defining a unique state of an immune system according to any one of claims 8 to 10, 26 or 27, the method for quantifying the frequency of disease-associated immune cells according to any one of claims 11 to 18, 26 or 27, the computer program product according to claim 19, 26 or 27, the method for predicting a clinical outcome according to any one of the claims 20, 22 to 26, the composition for use according to any one of claims 25 to 27 or method for determining susceptibility according to any one of the claims 21 to 24, 26, 27, wherein the pathogen is SARS-CoV-2.